{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:01:34Z","timestamp":1776888094825,"version":"3.51.2"},"reference-count":153,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s12145-019-00380-5","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T01:22:40Z","timestamp":1552008160000},"page":"143-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":340,"title":["Change detection techniques for remote sensing applications: a survey"],"prefix":"10.1007","volume":"12","author":[{"given":"Anju","family":"Asokan","sequence":"first","affiliation":[]},{"given":"J.","family":"Anitha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"key":"380_CR1","doi-asserted-by":"publisher","first-page":"482","DOI":"10.3390\/rs8060482","volume":"8","author":"O Ajadi","year":"2016","unstructured":"Ajadi O, Meyer F, Webley P (2016) Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sens 8:482. \n                    https:\/\/doi.org\/10.3390\/rs8060482","journal-title":"Remote Sens"},{"key":"380_CR2","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.renene.2016.05.066","volume":"97","author":"J Alonso-Montesinos","year":"2016","unstructured":"Alonso-Montesinos J, Mart\u00ednez-Durb\u00e1n M, del Sagrado J, del \u00c1guila IM, Batlles FJ (2016) The application of Bayesian network classifiers to cloud classification in satellite images. Renew Energy 97:155\u2013161. \n                    https:\/\/doi.org\/10.1016\/j.renene.2016.05.066","journal-title":"Renew Energy"},{"key":"380_CR3","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2017.02.008","volume":"126","author":"R Alshehhi","year":"2017","unstructured":"Alshehhi R, Marpu PR (2017) Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS J Photogramm Remote Sens 126:245\u2013260","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ejrs.2017.05.004","volume":"20","author":"G Amarnath","year":"2017","unstructured":"Amarnath G, Babar S, Sri M, Murthy R (2017) Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India \u2013 a multi-scale satellite remote sensing approach. Egypt J Remote Sensing Space Sci 20:157\u2013168","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR5","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ecoinf.2017.04.005","volume":"40","author":"V Amici","year":"2017","unstructured":"Amici V, Marcantonio M, La Porta N, Rocchini D (2017) A multi-temporal approach in MaxEnt modelling: a new frontier for land use\/land cover change detection. Ecol Inform 40:40\u201349. \n                    https:\/\/doi.org\/10.1016\/j.ecoinf.2017.04.005","journal-title":"Ecol Inform"},{"key":"380_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10661-018-6751-y","volume":"190","author":"F Aslami","year":"2018","unstructured":"Aslami F, Ghorbani A (2018) Object-based land-use \/ land-cover change detection using Landsat imagery\u00a0: a case study of Ardabil , Namin , and Nir counties in Northwest Iran. Environ Monit Assess 190:1\u201314. \n                    https:\/\/doi.org\/10.1007\/s10661-018-6751-y","journal-title":"Environ Monit Assess"},{"key":"380_CR7","doi-asserted-by":"publisher","first-page":"9065","DOI":"10.1109\/ACCESS.2017.2700405","volume":"5","author":"SA Azzouzi","year":"2017","unstructured":"Azzouzi SA, Vidal-Pantaleoni A, Bentounes HA (2017) Desertification monitoring in Biskra, Algeria, with Landsat imagery by means of supervised classification and change detection methods. IEEE Access 5:9065\u20139072. \n                    https:\/\/doi.org\/10.1109\/ACCESS.2017.2700405","journal-title":"IEEE Access"},{"key":"380_CR8","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/LGRS.2015.2433134","volume":"12","author":"J Barber","year":"2015","unstructured":"Barber J (2015) A generalized likelihood ratio test for coherent change detection in Polarimetric SAR. IEEE Geosci Remote Sens Lett 12:1873\u20131877. \n                    https:\/\/doi.org\/10.1109\/LGRS.2015.2433134","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"380_CR9","doi-asserted-by":"crossref","unstructured":"Berger A, Ettllin G, Quincke C, Rodriguez-Bocca P (2019) Predicting the normalized difference vegetation index(NDVI) by training a crop growth model with historical data. Comput Electron Agric:1\u20137","DOI":"10.1016\/j.compag.2018.04.028"},{"key":"380_CR10","doi-asserted-by":"crossref","unstructured":"Bhandari AK, Soni V, Kumar A, Singh GK (2014) Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT \u2013 SVD. ISA Trans:1\u201311","DOI":"10.1016\/j.isatra.2014.04.007"},{"key":"380_CR11","doi-asserted-by":"crossref","first-page":"8707","DOI":"10.1016\/j.eswa.2015.07.025","volume":"42","author":"AK Bhandari","year":"2015","unstructured":"Bhandari AK, Kumar A, Singh GK (2015a) Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst Appl 42:8707\u20138730","journal-title":"Expert Syst Appl"},{"key":"380_CR12","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1016\/j.eswa.2014.09.049","volume":"42","author":"AK Bhandari","year":"2015","unstructured":"Bhandari AK, Kumar A, Singh GK (2015b) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur\u2019s, Otsu and Tsallis functions. Expert Syst Appl 42:1573\u20131601","journal-title":"Expert Syst Appl"},{"key":"380_CR13","doi-asserted-by":"publisher","unstructured":"Bose S, Mukherjee A, Madhulika, Chakraborty S, Samanta S, Dey N (2013) Parallel image segmentation using multi-threading and k-means algorithm. IEEE Int Conf Comput Intell Comput Res:1\u20135. \n                    https:\/\/doi.org\/10.1109\/ICCIC.2013.6724171","DOI":"10.1109\/ICCIC.2013.6724171"},{"key":"380_CR14","doi-asserted-by":"publisher","first-page":"225","DOI":"10.5721\/EuJRS20164913","volume":"49","author":"G Cao","year":"2016","unstructured":"Cao G, Li X, Zhou L (2016a) Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model. Eur J Remote Sens 49:225\u2013237. \n                    https:\/\/doi.org\/10.5721\/EuJRS20164913","journal-title":"Eur J Remote Sens"},{"key":"380_CR15","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1080\/01431161.2016.1148284","volume":"37","author":"G Cao","year":"2016","unstructured":"Cao G, Zhou L, Li Y (2016b) A new change-detection method in high-resolution remote sensing images based on a conditional random field model. Int J Remote Sens 37:1173\u20131189. \n                    https:\/\/doi.org\/10.1080\/01431161.2016.1148284","journal-title":"Int J Remote Sens"},{"key":"380_CR16","doi-asserted-by":"publisher","unstructured":"Chen T, Trinder JC, Niu R (2017) Object-oriented landslide mapping using ZY-3 satellite imagery, random forest and mathematical morphology, for the three-gorges reservoir, China. Remote Sens 9. \n                    https:\/\/doi.org\/10.3390\/rs9040333","DOI":"10.3390\/rs9040333"},{"key":"380_CR17","doi-asserted-by":"publisher","first-page":"173","DOI":"10.5194\/isprs-annals-IV-1-29-2018","volume":"15","author":"K Chen","year":"2018","unstructured":"Chen K, Fu K, Yan M, Gao X, Sun X, Wei X (2018) Semantic segmentation of aerial images with shuffling convolutional neural networks. IEEE Geosci Remote Sens Lett 15:173\u2013177. \n                    https:\/\/doi.org\/10.5194\/isprs-annals-IV-1-29-2018","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"380_CR18","doi-asserted-by":"crossref","first-page":"28483","DOI":"10.1007\/s11042-018-6005-6","volume":"77","author":"S Chouhan","year":"2018","unstructured":"Chouhan S, Kaul A, Sharma U (2018) Soft computing approaches for image segmentation. Multimed Tools Appl 77:28483\u201328537","journal-title":"Multimed Tools Appl"},{"key":"380_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.infrared.2016.12.010","volume":"81","author":"B Cui","year":"2017","unstructured":"Cui B, Ma X, Xie X, Ren G, Ma Y (2017) Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrared Phys Technol 81:79\u201388. \n                    https:\/\/doi.org\/10.1016\/j.infrared.2016.12.010","journal-title":"Infrared Phys Technol"},{"key":"380_CR20","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.geomorph.2017.06.002","volume":"293","author":"B Feizizadeh","year":"2017","unstructured":"Feizizadeh B, Blaschke T, Tiede D, Moghaddam MHR (2017) Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology 293:240\u2013254. \n                    https:\/\/doi.org\/10.1016\/j.geomorph.2017.06.002","journal-title":"Geomorphology"},{"key":"380_CR21","doi-asserted-by":"publisher","unstructured":"Feng W, Sui H, Tu J, Huang W, Xu C, Sun K (2018) A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses. Remote Sens 10. \n                    https:\/\/doi.org\/10.3390\/rs10071015","DOI":"10.3390\/rs10071015"},{"key":"380_CR22","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TGRS.2017.2765348","volume":"56","author":"V Ferraris","year":"2018","unstructured":"Ferraris V, Dobigeon N, Wei Q, Chabert M (2018) Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach. IEEE Trans Geosci Remote Sens 56:1566\u20131578","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR23","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.uclim.2018.11.002","volume":"27","author":"LS Ferreira","year":"2019","unstructured":"Ferreira LS, Helena D, Duarte S (2019) Exploring the relationship between urban form , land surface temperature and vegetation indices in a subtropical megacity. Urban Clim 27:105\u2013123","journal-title":"Urban Clim"},{"key":"380_CR24","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.isprsjprs.2016.06.001","volume":"119","author":"AL Fytsilis","year":"2016","unstructured":"Fytsilis AL, Prokos A, Koutroumbas KD, Michail D, Kontoes CC (2016) A methodology for near real-time change detection between unmanned aerial vehicle and wide area satellite images. ISPRS J Photogramm Remote Sens 119:165\u2013186. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.06.001","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR25","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1016\/j.procs.2015.07.415","volume":"57","author":"M Gandhi","year":"2015","unstructured":"Gandhi M, Parthiban S, Thummalu N, Christy A (2015) Ndvi: vegetation change detection using remote sensing and gis \u2013 a case study of Vellore District. 3rd Int Conf Recent Trends Comput 57:1199\u20131210. \n                    https:\/\/doi.org\/10.1016\/j.procs.2015.07.415","journal-title":"3rd Int Conf Recent Trends Comput"},{"key":"380_CR26","doi-asserted-by":"publisher","first-page":"15353","DOI":"10.1007\/s11042-017-5120-0","volume":"77","author":"S Gandhimathi Alias Usha","year":"2018","unstructured":"Gandhimathi Alias Usha S, Vasuki S (2018) Improved segmentation and change detection of multi-spectral satellite imagery using graph cut based clustering and multiclass SVM. Multimed Tools Appl 77:15353\u201315383. \n                    https:\/\/doi.org\/10.1007\/s11042-017-5120-0","journal-title":"Multimed Tools Appl"},{"key":"380_CR27","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.geoderma.2016.01.012","volume":"268","author":"P Garc\u00eda","year":"2016","unstructured":"Garc\u00eda P, P\u00e9rez E (2016) Mapping of soil sealing by vegetation indexes and built-up index\u00a0: a case study in Madrid (Spain). Geoderma 268:100\u2013107","journal-title":"Geoderma"},{"key":"380_CR28","unstructured":"Garcia-jimenez S, Jurio A, Pagola M, De Miguel L, Barrenechea E, Bustince H (2016) Forest fire detection: a fuzzy system approach based on overlap indices. Appl Soft Comput:1\u20139"},{"key":"380_CR29","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.3390\/rs10081308","volume":"10","author":"A Garzelli","year":"2018","unstructured":"Garzelli A, Aiazzi B, Alparone L, Lolli S, Vivone G (2018) Multispectral Pansharpening with radiative transfer-based detail-injection modeling for preserving changes in vegetation cover. Remote Sens 10:1308. \n                    https:\/\/doi.org\/10.3390\/rs10081308","journal-title":"Remote Sens"},{"key":"380_CR30","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2016.10.010","volume":"122","author":"I Grinias","year":"2016","unstructured":"Grinias I, Panagiotakis C, Tziritas G (2016) MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS J Photogramm Remote Sens 122:145\u2013166","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR31","doi-asserted-by":"publisher","first-page":"17719","DOI":"10.1007\/s11042-015-2960-3","volume":"76","author":"W Gu","year":"2017","unstructured":"Gu W, Lv Z, Hao M (2017) Change detection method for remote sensing images based on an improved Markov random field. Multimed Tools Appl 76:17719\u201317734. \n                    https:\/\/doi.org\/10.1007\/s11042-015-2960-3","journal-title":"Multimed Tools Appl"},{"key":"380_CR32","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/15481603.2016.1246057","volume":"54","author":"M Han","year":"2017","unstructured":"Han M, Zhou Y (2017) An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing. GIScience Remote Sens 54:20\u201337. \n                    https:\/\/doi.org\/10.1080\/15481603.2016.1246057","journal-title":"GIScience Remote Sens"},{"key":"380_CR33","doi-asserted-by":"publisher","first-page":"4276","DOI":"10.1080\/01431161.2016.1210838","volume":"37","author":"M Hao","year":"2016","unstructured":"Hao M, Shi W, Deng K, Feng Q (2016) Superpixel-based active contour model for unsupervised change detection from satellite images. Int J Remote Sens 37:4276\u20134295. \n                    https:\/\/doi.org\/10.1080\/01431161.2016.1210838","journal-title":"Int J Remote Sens"},{"key":"380_CR34","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.ejrs.2016.12.003","volume":"20","author":"I Haque","year":"2017","unstructured":"Haque I, Basak R (2017) Land cover change detection using GIS and remote sensing techniques\u00a0: a spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egypt J Remote Sensing Space Sci 20:251\u2013263","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR35","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1080\/2150704X.2014.912766","volume":"5","author":"P He","year":"2014","unstructured":"He P, Shi W, Zhang H, Hao M (2014) A novel dynamic threshold method for unsupervised change detection from remotely sensed images. Remote Sens Lett 5:396\u2013403. \n                    https:\/\/doi.org\/10.1080\/2150704X.2014.912766","journal-title":"Remote Sens Lett"},{"key":"380_CR36","doi-asserted-by":"publisher","unstructured":"He P, Shi W, Miao Z, Zhang H, Cai L (2015) Advanced MarkRemote Sens Lettov random field model based on local uncertainty for unsupervised change detection. 6:667\u2013676. \n                    https:\/\/doi.org\/10.1080\/2150704X.2015.1054045","DOI":"10.1080\/2150704X.2015.1054045"},{"key":"380_CR37","unstructured":"Helmy AK, El-Taweel GS (2015) Image segmentation scheme based on SOM\u2013PCNN in frequency domain. Appl Soft Comput J:1\u201311"},{"key":"380_CR38","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s12145-015-0217-3","volume":"8","author":"D H\u00f6lbling","year":"2015","unstructured":"H\u00f6lbling D, Friedl B, Eisank C (2015) An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Sci Inf 8:327\u2013335. \n                    https:\/\/doi.org\/10.1007\/s12145-015-0217-3","journal-title":"Earth Sci Inf"},{"key":"380_CR39","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1080\/02664763.2014.932761","volume":"42","author":"L Holmstr\u00f6m","year":"2015","unstructured":"Holmstr\u00f6m L, Pasanen L (2015) Bayesian scale space analysis of temporal changes in satellite images. J Appl Stat 42:50\u201370. \n                    https:\/\/doi.org\/10.1080\/02664763.2014.932761","journal-title":"J Appl Stat"},{"key":"380_CR40","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.11591\/ijece.v6i6.11801","volume":"6","author":"S Hore","year":"2016","unstructured":"Hore S, Chakraborty S, Chatterjee S, Dey N (2016) An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. Int J Electr Comput Eng 6:2773\u20132780. \n                    https:\/\/doi.org\/10.11591\/ijece.v6i6.11801","journal-title":"Int J Electr Comput Eng"},{"key":"380_CR41","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.dsp.2012.12.011","volume":"23","author":"P Hoseini","year":"2013","unstructured":"Hoseini P, Shayesteh MG (2013) Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digit Signal Process 23:879\u2013893","journal-title":"Digit Signal Process"},{"key":"380_CR42","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","volume":"214","author":"B Huang","year":"2018","unstructured":"Huang B, Zhao B, Song Y (2018a) Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ 214:73\u201386","journal-title":"Remote Sens Environ"},{"key":"380_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-018-7334-5","volume":"77","author":"F Huang","year":"2018","unstructured":"Huang F, Chen L, Yin K, Huang J, Gui L (2018b) Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao landslide, three gorges reservoir, China. Environ Earth Sci 77:1\u201319. \n                    https:\/\/doi.org\/10.1007\/s12665-018-7334-5","journal-title":"Environ Earth Sci"},{"key":"380_CR44","unstructured":"Huang F, Yu Y, Feng T (2018c) Hyperspectral remote sensing image change detection based on tensor and deep learning. J Vis Commun Image Represent:2\u201324"},{"key":"380_CR45","doi-asserted-by":"publisher","unstructured":"Huang Z, Huang L, Li Q, Zhang T, Sang N (2018d) Framelet regularization for uneven intensity correction of color images with illumination and reflectance estimation. Neurocomputing:3\u201324. \n                    https:\/\/doi.org\/10.1016\/j.neucom.2018.06.063","DOI":"10.1016\/j.neucom.2018.06.063"},{"key":"380_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19479832.2018.1491897","volume":"9832","author":"S Iino","year":"2018","unstructured":"Iino S, Ito R, Doi K, Imaizumi T, Hikosaka S (2018) CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring. Int J Image Data Fusion 9832:1\u201317. \n                    https:\/\/doi.org\/10.1080\/19479832.2018.1491897","journal-title":"Int J Image Data Fusion"},{"key":"380_CR47","doi-asserted-by":"publisher","first-page":"521","DOI":"10.14358\/PERS.82.7.521","volume":"82","author":"S Jabari","year":"2016","unstructured":"Jabari S, Zhang Y (2016) RPC-based Coregistration of VHR imagery for urban change detection. Photogramm Eng Remote Sens 82:521\u2013534. \n                    https:\/\/doi.org\/10.14358\/PERS.82.7.521","journal-title":"Photogramm Eng Remote Sens"},{"key":"380_CR48","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.compag.2016.03.008","volume":"123","author":"L Jiang","year":"2016","unstructured":"Jiang L, Shang S, Yang Y, Guan H (2016) Mapping interannual variability of maize cover in a large irrigation district using a vegetation index \u2013 phenological index classifier. Comput Electron Agric 123:351\u2013361","journal-title":"Comput Electron Agric"},{"key":"380_CR49","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compenvurbsys.2017.02.002","volume":"64","author":"BA Johnson","year":"2017","unstructured":"Johnson BA, Iizuka K, Bragais MA, Endo I, Magcale-macandog DB (2017) Employing crowdsourced geographic data and multi-temporal \/ multi-sensor satellite imagery to monitor land cover change\u00a0: a case study in an urbanizing region of the Philippines. Comput Environ Urban Syst 64:184\u2013193","journal-title":"Comput Environ Urban Syst"},{"key":"380_CR50","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ejrs.2017.04.003","volume":"20","author":"S Kaliraj","year":"2017","unstructured":"Kaliraj S, Chandrasekar N, Ramachandran KK, Srinivas Y, Saravanan S (2017) Coastal landuse and land cover change and transformations of Kanyakumari coast , India using remote sensing and GIS. Egypt J Remote Sensing Space Sci 20:169\u2013185","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR51","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ejrs.2016.04.003","volume":"20","author":"K Kant","year":"2017","unstructured":"Kant K, Singh A (2017) Identification of flooded area from satellite images using hybrid Kohonen fuzzy C-means sigma classifier. Egypt J Remote Sensing Space Sci 20:147\u2013155","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR52","doi-asserted-by":"crossref","unstructured":"Kapoor S, Zeya I, Singhal C, Nanda SJ (2017) A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation. 7th Int Conf Adv Comput Commun ICACC-2017 115:415\u201322","DOI":"10.1016\/j.procs.2017.09.100"},{"key":"380_CR53","doi-asserted-by":"publisher","first-page":"27442","DOI":"10.1109\/ACCESS.2018.2807380","volume":"6","author":"L Ke","year":"2018","unstructured":"Ke L, Lin Y, Zeng Z, Zhang L, Meng L (2018) Adaptive change detection with significance test. IEEE Access 6:27442\u201327450. \n                    https:\/\/doi.org\/10.1109\/ACCESS.2018.2807380","journal-title":"IEEE Access"},{"key":"380_CR54","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jag.2017.10.007","volume":"65","author":"JT Kelly","year":"2018","unstructured":"Kelly JT, Gontz AM (2018) Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices. Int J Appl Earth Obs Geoinf 65:92\u2013104","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR55","doi-asserted-by":"publisher","unstructured":"Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl:3\u201334. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2017.04.029","DOI":"10.1016\/j.eswa.2017.04.029"},{"key":"380_CR56","doi-asserted-by":"publisher","first-page":"5407","DOI":"10.1109\/TGRS.2017.2707528","volume":"55","author":"SH Khan","year":"2017","unstructured":"Khan SH, He X, Porikli F, Bennamoun M (2017) Forest change detection in incomplete satellite images with deep neural networks. IEEE Trans Geosci Remote Sens 55:5407\u20135423. \n                    https:\/\/doi.org\/10.1109\/TGRS.2017.2707528","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR57","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.jag.2015.06.004","volume":"42","author":"W Kleynhans","year":"2015","unstructured":"Kleynhans W, Salmon BP, Olivier JC (2015) Detecting settlement expansion in South Africa using a hyper-temporal SAR change detection approach. Int J Appl Earth Obs Geoinf 42:142\u2013149. \n                    https:\/\/doi.org\/10.1016\/j.jag.2015.06.004","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR58","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jag.2015.04.009","volume":"40","author":"W Kleynhansa","year":"2015","unstructured":"Kleynhansa W, Salmon BP, Wessels KJ, Olivier JC (2015) Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method. Int J Appl Earth Obs Geoinf 40:74\u201380. \n                    https:\/\/doi.org\/10.1016\/j.jag.2015.04.009","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR59","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1016\/j.eswa.2013.10.059","volume":"41","author":"A Kumar","year":"2014","unstructured":"Kumar A, Kumar V, Kumar A, Kumar G (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur\u2019s entropy. Expert Syst Appl 41:3538\u20133560","journal-title":"Expert Syst Appl"},{"key":"380_CR60","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.3390\/rs10091381","volume":"10","author":"T Lei","year":"2018","unstructured":"Lei T, Xue D, Lv Z, Li S, Zhang Y, Nandi AK (2018) Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images. Remote Sens 10:1381. \n                    https:\/\/doi.org\/10.3390\/rs10091381","journal-title":"Remote Sens"},{"key":"380_CR61","unstructured":"Li H, Gong M, Wang Q, Liu J, Su L (2015) A multiobjective fuzzy clustering method for change detection in SAR images. Appl Soft Comput:1\u201311"},{"key":"380_CR62","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.ecolind.2015.11.005","volume":"62","author":"F Li","year":"2016","unstructured":"Li F, Zeng Y, Luo J, Ma R, Wu B (2016a) Modeling grassland aboveground biomass using a pure vegetation index. Ecol Indic 62:279\u2013288","journal-title":"Ecol Indic"},{"key":"380_CR63","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","volume":"175","author":"Z Li","year":"2016","unstructured":"Li Z, Shi W, Myint SW, Lu P, Wang Q (2016b) Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sens Environ 175:215\u2013230. \n                    https:\/\/doi.org\/10.1016\/j.rse.2016.01.003","journal-title":"Remote Sens Environ"},{"key":"380_CR64","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.3390\/rs10071129","volume":"10","author":"Y Lin","year":"2018","unstructured":"Lin Y, Yu J, Cai J, Sneeuw N, Li F (2018) Spatio-temporal analysis of wetland changes using a kernel extreme learning machine approach. Remote Sens 10:1129. \n                    https:\/\/doi.org\/10.3390\/rs10071129","journal-title":"Remote Sens"},{"key":"380_CR65","doi-asserted-by":"publisher","first-page":"4363","DOI":"10.1109\/TGRS.2015.2396686","volume":"53","author":"S Liu","year":"2015","unstructured":"Liu S, Bruzzone L, Bovolo F, Zanetti M, Du P (2015) Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Trans Geosci Remote Sens 53:4363\u20134378. \n                    https:\/\/doi.org\/10.1109\/TGRS.2015.2396686","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR66","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.isprsjprs.2017.02.016","volume":"128","author":"C Liu","year":"2017","unstructured":"Liu C, Cheng I, Zhang Y, Basu A (2017) Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency. ISPRS J Photogramm Remote Sens 128:16\u201326","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR67","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TNNLS.2016.2636227","volume":"29","author":"J Liu","year":"2018","unstructured":"Liu J, Gong M, Qin K, Zhang P (2018a) A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans Neural Networks Learn Syst 29:545\u2013559. \n                    https:\/\/doi.org\/10.1109\/TNNLS.2016.2636227","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"380_CR68","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TGRS.2017.2743243","volume":"56","author":"Q Liu","year":"2018","unstructured":"Liu Q, Hang R, Song H, Li Z (2018b) Learning multiscale deep features for high-resolution satellite image scene classification. IEEE Trans Geosci Remote Sens 56:117\u2013126. \n                    https:\/\/doi.org\/10.1109\/TGRS.2017.2743243","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR69","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.rse.2018.06.031","volume":"216","author":"T Liu","year":"2018","unstructured":"Liu T, Abd-Elrahman A, Zare A, Dewitt BA, Flory L, Smith SE (2018c) A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sens Environ 216:328\u2013344. \n                    https:\/\/doi.org\/10.1016\/j.rse.2018.06.031","journal-title":"Remote Sens Environ"},{"key":"380_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s18103232","volume":"18","author":"Y Liu","year":"2018","unstructured":"Liu Y, Ren Q, Geng J, Ding M (2018d) Efficient patch-wise semantic segmentation for large-scale remote sensing images. Sensors 18:1\u201316. \n                    https:\/\/doi.org\/10.3390\/s18103232","journal-title":"Sensors"},{"key":"380_CR71","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TIP.2017.2784560","volume":"27","author":"Z Liu","year":"2018","unstructured":"Liu Z, Li G, Mercier G, He Y, Pan Q (2018e) Change detection in Heterogenous remote sensing images via homogeneous pixel transformation. IEEE Trans Image Process 27:1822\u20131834. \n                    https:\/\/doi.org\/10.1109\/TIP.2017.2784560","journal-title":"IEEE Trans Image Process"},{"key":"380_CR72","doi-asserted-by":"publisher","unstructured":"Lu M, Hamunyela E, Verbesselt J, Pebesma E (2017) Dimension reduction of multi-spectral satellite image time series to improve deforestation monitoring. Remote Sens 9. \n                    https:\/\/doi.org\/10.3390\/rs9101025","DOI":"10.3390\/rs9101025"},{"key":"380_CR73","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.aeue.2015.11.004","volume":"70","author":"X Luo","year":"2016","unstructured":"Luo X, Zhang Z, Wu X (2016) A novel algorithm of remote sensing image fusion based on shift-invariant Shearlet transform and regional selection. Int J Electron Commun (AE\u00dc) 70:186\u2013197","journal-title":"Int J Electron Commun (AE\u00dc)"},{"key":"380_CR74","doi-asserted-by":"publisher","first-page":"980","DOI":"10.3390\/rs10070980","volume":"10","author":"H Luo","year":"2018","unstructured":"Luo H, Liu C, Wu C, Guo X (2018) Urban change detection based on Dempster\u2013Shafer theory for multitemporal very high-resolution imagery. Remote Sens 10:980. \n                    https:\/\/doi.org\/10.3390\/rs10070980","journal-title":"Remote Sens"},{"key":"380_CR75","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1109\/LGRS.2016.2619163","volume":"13","author":"P Lv","year":"2016","unstructured":"Lv P, Zhong Y, Zhao J, Jiao H, Zhang L (2016) Change detection based on a multifeature probabilistic ensemble conditional random field model for high spatial resolution remote sensing imagery. IEEE Geosci Remote Sens Lett 13:1965\u20131969","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"380_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijgi6100310","volume":"6","author":"C Ma","year":"2017","unstructured":"Ma C, Xia W, Chen F, Liu J, Dai Q, Jiang L et al (2017) A content-based remote sensing image change information retrieval model. Isprs Int J Geo-Information 6:1\u201317. \n                    https:\/\/doi.org\/10.3390\/ijgi6100310","journal-title":"Isprs Int J Geo-Information"},{"key":"380_CR77","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.ecolind.2018.07.050","volume":"95","author":"Q Ma","year":"2018","unstructured":"Ma Q, Su Y, Luo L, Li L, Kelly M, Guo Q (2018) Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecol Indic 95:298\u2013310","journal-title":"Ecol Indic"},{"key":"380_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/Multi-Temp.2017.8035239","volume":"2017","author":"D Marinelli","year":"2017","unstructured":"Marinelli D, Bovolo F, Bruzzone L (2017) A novel method for unsupervised multiple change detection in hyperspectral images based on binary spectral change vectors. 2017 9th Int work anal multitemporal remote Sens images. MultiTemp 2017:1\u20134. \n                    https:\/\/doi.org\/10.1109\/Multi-Temp.2017.8035239","journal-title":"MultiTemp"},{"key":"380_CR79","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","volume":"135","author":"D Marmanis","year":"2018","unstructured":"Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U (2018) Classification with an edge\u00a0: improving semantic image segmentation with boundary detection. ISPRS J Photogramm Remote Sens 135:158\u2013172","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR80","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1080\/01431161.2017.1390272","volume":"39","author":"C Massarelli","year":"2018","unstructured":"Massarelli C (2018) Fast detection of significantly transformed areas due to illegal waste burial with a procedure applicable to landsat images. Int J Remote Sens 39:754\u2013769. \n                    https:\/\/doi.org\/10.1080\/01431161.2017.1390272","journal-title":"Int J Remote Sens"},{"key":"380_CR81","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/LGRS.2018.2794545","volume":"15","author":"Z Miao","year":"2018","unstructured":"Miao Z, Fu K, Sun H, Sun X, Yan M (2018) Automatic water-body segmentation from high-resolution satellite images via deep networks. IEEE Geosci Remote Sens Lett 15:602\u2013606. \n                    https:\/\/doi.org\/10.1109\/LGRS.2018.2794545","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"380_CR82","doi-asserted-by":"publisher","unstructured":"Minu S, Shetty A (2015) A Comparative Study of Image Change Detection Algorithms in MATLAB. Int. Conf. WATER Resour. Coast. Ocean Eng. (ICWRCOE 2015), Aquat. Procedia, vol. 4, p. 1366\u201373. \n                    https:\/\/doi.org\/10.1016\/j.aqpro.2015.02.177","DOI":"10.1016\/j.aqpro.2015.02.177"},{"key":"380_CR83","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.engappai.2018.03.001","volume":"71","author":"H Mittal","year":"2018","unstructured":"Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng Appl Artif Intell 71:226\u2013235","journal-title":"Eng Appl Artif Intell"},{"key":"380_CR84","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2016.12.003","volume":"190","author":"A Mohammadi","year":"2017","unstructured":"Mohammadi A, Costelloe JF, Ryu D (2017) Application of time series of remotely sensed normalized difference water , vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone fl oodplains. Remote Sens Environ 190:70\u201382","journal-title":"Remote Sens Environ"},{"key":"380_CR85","unstructured":"Naidu MSR, Kumar PR, Chiranjeevi K (2017) Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Eng J:1\u201313"},{"key":"380_CR86","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.optlastec.2013.10.003","volume":"57","author":"B Narayan","year":"2014","unstructured":"Narayan B, Bovolo F, Ghosh A, Bruzzone L (2014) Spatio-contextual fuzzy clustering with Markov random fi eld model for change detection in remotely sensed images. Opt Laser Technol 57:284\u2013292","journal-title":"Opt Laser Technol"},{"key":"380_CR87","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-017-7133-4","volume":"76","author":"BK Pandey","year":"2017","unstructured":"Pandey BK, Khare D (2017) Analyzing and modeling of a large river basin dynamics applying integrated cellular automata and Markov model. Environ Earth Sci 76:1\u201312. \n                    https:\/\/doi.org\/10.1007\/s12665-017-7133-4","journal-title":"Environ Earth Sci"},{"key":"380_CR88","doi-asserted-by":"publisher","first-page":"3301","DOI":"10.3390\/su10093301","volume":"10","author":"H Park","year":"2018","unstructured":"Park H, Choi J, Park W, Park H (2018) Modified S2CVA algorithm using cross-sharpened images for unsupervised change detection. Sustainability 10:3301. \n                    https:\/\/doi.org\/10.3390\/su10093301","journal-title":"Sustainability"},{"key":"380_CR89","doi-asserted-by":"publisher","first-page":"5592","DOI":"10.1080\/01431161.2017.1343512","volume":"38","author":"SD Patil","year":"2017","unstructured":"Patil SD, Gu Y, Dias FSA, Stieglitz M, Turk G (2017) Predicting the spectral information of future land cover using machine learning. Int J Remote Sens 38:5592\u20135607. \n                    https:\/\/doi.org\/10.1080\/01431161.2017.1343512","journal-title":"Int J Remote Sens"},{"key":"380_CR90","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.03.013","volume":"128","author":"LTH Pham","year":"2017","unstructured":"Pham LTH, Brabyn L (2017) Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J Photogramm Remote Sens 128:86\u201397. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2017.03.013","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR91","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1109\/TIP.2017.2766358","volume":"27","author":"R Pradhan","year":"2018","unstructured":"Pradhan R, Aygun RS, Maskey M, Ramachandran R, Cecil DJ (2018) Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans Image Process 27:692\u2013702. \n                    https:\/\/doi.org\/10.1109\/TIP.2017.2766358","journal-title":"IEEE Trans Image Process"},{"key":"380_CR92","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.wace.2016.08.001","volume":"13","author":"S Prakash","year":"2016","unstructured":"Prakash S, Kumar A (2016) Evaluation of course change detection of Ramganga river using remote sensing and GIS, India. Weather Clim Extrem 13:68\u201372","journal-title":"Weather Clim Extrem"},{"key":"380_CR93","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TIP.2014.2387013","volume":"24","author":"J Prendes","year":"2015","unstructured":"Prendes J, Chabert M, Pascal F, Giros A, Tourneret J-Y (2015) A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors. IEEE Trans Image Process 24:799\u2013812. \n                    https:\/\/doi.org\/10.1109\/TIP.2014.2387013","journal-title":"IEEE Trans Image Process"},{"key":"380_CR94","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.isprsjprs.2015.02.004","volume":"107","author":"Z Qi","year":"2015","unstructured":"Qi Z, Yeh AG-O, Li X, Zhang X (2015) A three-component method for timely detection of land cover changes using polarimetric SAR images. ISPRS J Photogramm Remote Sens 107:3\u201321. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2015.02.004","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR95","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.eswa.2017.12.038","volume":"97","author":"D Qin","year":"2018","unstructured":"Qin D, Zhou X, Zhou W, Huang G, Ren Y, Horan B et al (2018) MSIM: a change detection framework for damage assessment in natural disasters. Expert Syst Appl 97:372\u2013383. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2017.12.038","journal-title":"Expert Syst Appl"},{"key":"380_CR96","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.isprsjprs.2017.10.003","volume":"133","author":"B Qiu","year":"2017","unstructured":"Qiu B, Chen G, Tang Z, Lu D, Wang Z, Chen C (2017) Assessing the three-north shelter Forest program in China by a novel framework for characterizing vegetation changes. ISPRS J Photogramm Remote Sens 133:75\u201388","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR97","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jvcir.2016.11.006","volume":"42","author":"JVCI R","year":"2017","unstructured":"R JVCI, Hagag A, Fan X, El-samie FEA (2017) HyperCast\u00a0: hyperspectral satellite image broadcasting with band ordering optimization. J Vis Commun Image Represent 42:14\u201327","journal-title":"J Vis Commun Image Represent"},{"key":"380_CR98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/23311916.2018.1484587","volume":"5","author":"K Radhika","year":"2018","unstructured":"Radhika K, Varadarajan S (2018) A neural network based classification of satellite images for change detection applications. Cogent Eng 5:1\u20139","journal-title":"Cogent Eng"},{"key":"380_CR99","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.ejrs.2017.12.002","volume":"21","author":"M Rahbani","year":"2018","unstructured":"Rahbani M, Pakhirehzan M (2018) Classifying east coasts of Hormozgan province using Shepard method and satellite imagery. Egypt J Remote Sensing Space Sci 21:335\u2013344. \n                    https:\/\/doi.org\/10.1016\/j.ejrs.2017.12.002","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR100","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ejrs.2015.02.002","volume":"18","author":"JS Rawat","year":"2015","unstructured":"Rawat JS, Kumar M (2015) Monitoring land use \/ cover change using remote sensing and GIS techniques\u00a0: a case study of Hawalbagh block , district Almora, Uttarakhand, India. Egypt J Remote Sensing Space Sci 18:77\u201384","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-015-2301-x","volume":"9","author":"V Sadeghi","year":"2016","unstructured":"Sadeghi V, Farnood Ahmadi F, Ebadi H (2016) Design and implementation of an expert system for updating thematic maps using satellite imagery (case study: changes of Lake Urmia). Arab J Geosci 9:1\u201317. \n                    https:\/\/doi.org\/10.1007\/s12517-015-2301-x","journal-title":"Arab J Geosci"},{"key":"380_CR102","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.measurement.2018.05.097","volume":"127","author":"V Sadeghi","year":"2018","unstructured":"Sadeghi V, Farnood Ahmadi F, Ebadi H (2018) A new fuzzy measurement approach for automatic change detection using remotely sensed images. Meas J Int Meas Confed 127:1\u201314. \n                    https:\/\/doi.org\/10.1016\/j.measurement.2018.05.097","journal-title":"Meas J Int Meas Confed"},{"key":"380_CR103","doi-asserted-by":"publisher","first-page":"7165","DOI":"10.1109\/TGRS.2017.2743218","volume":"55","author":"BP Salmon","year":"2017","unstructured":"Salmon BP, Holloway DS, Kleynhans W, Olivier JC, Wessels KJ (2017) Applying model parameters as a driving force to a deterministic nonlinear system to detect land cover change. IEEE Trans Geosci Remote Sens 55:7165\u20137176. \n                    https:\/\/doi.org\/10.1109\/TGRS.2017.2743218","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR104","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.chb.2013.06.025","volume":"30","author":"R Sammouda","year":"2014","unstructured":"Sammouda R, Adgaba N, Touir A, Al-ghamdi A (2014) Agriculture satellite image segmentation using a modified artificial Hopfield neural network. Comput Hum Behav 30:436\u2013441","journal-title":"Comput Hum Behav"},{"key":"380_CR105","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s12518-018-0206-6","volume":"10","author":"ST Seydi","year":"2018","unstructured":"Seydi ST, Hasanlou M (2018) Sensitivity analysis of pansharpening in hyperspectral change detection. Appl Geomatics 10:65\u201375. \n                    https:\/\/doi.org\/10.1007\/s12518-018-0206-6","journal-title":"Appl Geomatics"},{"key":"380_CR106","first-page":"1","volume":"1","author":"M Shakeri","year":"2016","unstructured":"Shakeri M, Dezfoulian MH, Khotanlou H, Barati AH, Masoumi Y (2016) Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digit Signal Process 1:1\u201313","journal-title":"Digit Signal Process"},{"key":"380_CR107","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-016-0397-0","volume":"2016","author":"A Shi","year":"2016","unstructured":"Shi A, Gao G, Shen S (2016) Change detection of bitemporal multispectral images based on FCM and D-S theory. EURASIP J Adv Signal Process 2016:1\u201312. \n                    https:\/\/doi.org\/10.1186\/s13634-016-0397-0","journal-title":"EURASIP J Adv Signal Process"},{"key":"380_CR108","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.jvcir.2016.11.017","volume":"42","author":"A Singh","year":"2017","unstructured":"Singh A, Singh KK (2017) Satellite image classification using genetic algorithm trained radial basis function neural network, application to the detection of flooded areas. J Vis Commun Image Represent 42:173\u2013181. \n                    https:\/\/doi.org\/10.1016\/j.jvcir.2016.11.017","journal-title":"J Vis Commun Image Represent"},{"key":"380_CR109","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs10040533","volume":"10","author":"YT Solano-Correa","year":"2018","unstructured":"Solano-Correa YT, Bovolo F, Bruzzone L (2018) An approach for unsupervised change detection in multitemporal VHR images acquired by different multispectral sensors. Remote Sens 10:1\u201323. \n                    https:\/\/doi.org\/10.3390\/rs10040533","journal-title":"Remote Sens"},{"key":"380_CR110","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jag.2017.01.015","volume":"58","author":"W Song","year":"2017","unstructured":"Song W, Mu X, Ruan G, Gao Z, Li L, Yan G (2017) Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. Int J Appl Earth Obs Geoinf 58:168\u2013176","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR111","doi-asserted-by":"crossref","unstructured":"Su L, Gong M, Zhang P, Zhang M, Liu J, Yang H (2017) Deep learning and mapping based ternary change detection for information unbalanced images. Pattern Recogn:2\u201342","DOI":"10.1016\/j.patcog.2017.01.002"},{"key":"380_CR112","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1049\/iet-rsn.2017.0393","volume":"12","author":"MN Sumaiya","year":"2018","unstructured":"Sumaiya MN, Kumari RSS (2018) Unsupervised change detection of flood affected areas in SAR images using Rayleigh based Bayesian thresholding. IET Radar, Sonar Navig 12:515\u2013522. \n                    https:\/\/doi.org\/10.1049\/iet-rsn.2017.0393","journal-title":"IET Radar, Sonar Navig"},{"key":"380_CR113","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ijleo.2016.11.040","volume":"130","author":"MN Sumaiya","year":"2017","unstructured":"Sumaiya MN, Shantha Selva Kumari R (2017a) Gabor filter based change detection in SAR images by KI thresholding. Optik (Stuttg) 130:114\u2013122. \n                    https:\/\/doi.org\/10.1016\/j.ijleo.2016.11.040","journal-title":"Optik (Stuttg)"},{"key":"380_CR114","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1007\/s11277-017-4741-y","volume":"97","author":"MN Sumaiya","year":"2017","unstructured":"Sumaiya MN, Shantha Selva Kumari R (2017b) Satellite image change detection using Laplacian\u2013Gaussian distributions. Wirel Pers Commun 97:4621\u20134630. \n                    https:\/\/doi.org\/10.1007\/s11277-017-4741-y","journal-title":"Wirel Pers Commun"},{"key":"380_CR115","doi-asserted-by":"publisher","unstructured":"Sun H, Wang Q, Wang G, Lin H, Luo P, Li J et al (2018) Optimizing kNN for mapping vegetation cover of arid and semi-arid areas using landsat images. Remote Sens 10. \n                    https:\/\/doi.org\/10.3390\/rs10081248","DOI":"10.3390\/rs10081248"},{"key":"380_CR116","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.eswa.2016.03.032","volume":"58","author":"S Suresh","year":"2016","unstructured":"Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184\u2013209","journal-title":"Expert Syst Appl"},{"key":"380_CR117","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.asoc.2017.08.019","volume":"61","author":"S Suresh","year":"2017","unstructured":"Suresh S, Lal S (2017a) Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl Soft Comput J 61:622\u2013641. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2017.08.019","journal-title":"Appl Soft Comput J"},{"key":"380_CR118","doi-asserted-by":"publisher","unstructured":"Suresh S, Lal S (2017b) Multilevel thresholding based on chaotic Darwinian particle swarm optimization for segmentation of satellite images. Appl Soft Comput:2\u201340. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2017.02.005","DOI":"10.1016\/j.asoc.2017.02.005"},{"key":"380_CR119","doi-asserted-by":"publisher","unstructured":"Swain S, Abeysundara S, Hayhoe K, Stoner AMK (2017) Future changes in summer MODIS-based enhanced vegetation index for the south-Central United States. Ecol Inform:3\u201333. \n                    https:\/\/doi.org\/10.1016\/j.ecoinf.2017.07.007","DOI":"10.1016\/j.ecoinf.2017.07.007"},{"key":"380_CR120","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.jag.2017.08.006","volume":"64","author":"S Testa","year":"2018","unstructured":"Testa S, Soudani K, Boschetti L, Borgogno Mondino E, EVI MODIS-d (2018) NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. Int J Appl Earth Obs Geoinf 64:132\u2013144. \n                    https:\/\/doi.org\/10.1016\/j.jag.2017.08.006","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR121","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-015-2267-8","volume":"9","author":"AK Thakkar","year":"2016","unstructured":"Thakkar AK, Desai VR, Patel A, Potdar MB (2016) An effective hybrid classification approach using tasseled cap transformation (TCT) for improving classification of land use\/land cover (LU\/LC) in semi-arid region: a case study of Morva-Hadaf watershed, Gujarat, India. Arab J Geosci 9:1\u201313. \n                    https:\/\/doi.org\/10.1007\/s12517-015-2267-8","journal-title":"Arab J Geosci"},{"key":"380_CR122","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.ejrs.2016.11.006","volume":"20","author":"AK Thakkar","year":"2017","unstructured":"Thakkar AK, Desai VR, Patel A, Potdar MB (2017) Post-classification corrections in improving the classification of land use\/land cover of arid region using RS and GIS: the case of Arjuni watershed, Gujarat, India. Egypt J Remote Sens Sp Sci 20:79\u201389. \n                    https:\/\/doi.org\/10.1016\/j.ejrs.2016.11.006","journal-title":"Egypt J Remote Sens Sp Sci"},{"key":"380_CR123","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.ins.2018.08.015","volume":"467","author":"D Tian","year":"2018","unstructured":"Tian D, Gong M (2018) A novel edge-weight based fuzzy clustering method for change detection in SAR images. Inf Sci (Ny) 467:415\u2013430. \n                    https:\/\/doi.org\/10.1016\/j.ins.2018.08.015","journal-title":"Inf Sci (Ny)"},{"key":"380_CR124","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1109\/TGRS.2017.2758359","volume":"56","author":"R Touati","year":"2018","unstructured":"Touati R, Mignotte M (2018) An energy-based model encoding nonlocal pairwise pixel interactions for multisensor change detection. IEEE Trans Geosci Remote Sens 56:1046\u20131058. \n                    https:\/\/doi.org\/10.1109\/TGRS.2017.2758359","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR125","doi-asserted-by":"crossref","unstructured":"Tuba M, Jordanski M, Arsic A (2016) Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm","DOI":"10.1016\/B978-0-12-804536-7.00004-1"},{"key":"380_CR126","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.ejrs.2017.05.001","volume":"20","author":"F Uchenna","year":"2017","unstructured":"Uchenna F, Timipre R, Chigozie E, Okpala-okaka C (2017) Geospatial assessment of vegetation status in Sagbama oilfield environment in the Niger Delta region, Nigeria. Egypt J Remote Sensing Space Sci 20:211\u2013221","journal-title":"Egypt J Remote Sensing Space Sci"},{"key":"380_CR127","doi-asserted-by":"publisher","first-page":"016016","DOI":"10.1117\/1.JRS.11.016016","volume":"11","author":"R V\u00e1zquez-jim\u00e9nez","year":"2017","unstructured":"V\u00e1zquez-jim\u00e9nez R, Romero-calcerrada R, Novillo CJ, Ramos-bernal RN, Arrogante-funes P (2017) Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods. J Appl Remote Sens 11:016016(1-14). \n                    https:\/\/doi.org\/10.1117\/1.JRS.11.016016","journal-title":"J Appl Remote Sens"},{"key":"380_CR128","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2016.10.001","volume":"187","author":"SM Vicente-serrano","year":"2016","unstructured":"Vicente-serrano SM, Camarero JJ, Olano JM, Mart\u00edn-hern\u00e1ndez N, Pe\u00f1a-gallardo M, Tom\u00e1s-burguera M et al (2016) Diverse relationships between forest growth and the normalized difference vegetation index at a global scale. Remote Sens Environ 187:14\u201329","journal-title":"Remote Sens Environ"},{"key":"380_CR129","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i42\/99682","volume":"9","author":"T Vignesh","year":"2016","unstructured":"Vignesh T, Thyagharajan KK, Murugan D, Sakthivel M, Pushparaj S (2016) A novel multiple unsupervised algorithm for land use\/land cover classification. Indian J Sci Technol 9:1\u201312. \n                    https:\/\/doi.org\/10.17485\/ijst\/2016\/v9i42\/99682","journal-title":"Indian J Sci Technol"},{"key":"380_CR130","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2018.06.007","volume":"144","author":"M Volpi","year":"2018","unstructured":"Volpi M, Tuia D (2018) Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images. ISPRS J Photogramm Remote Sens 144:48\u201360","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR131","first-page":"1","volume":"99","author":"X Wan","year":"2018","unstructured":"Wan X, Liu J, Li S, Dawson J, Yan H (2018) An illumination-invariant change detection method based on disparity saliency map for multitemporal optical remotely sensed images. IEEE Trans Geosci Remote Sens 99:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"380_CR132","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1109\/JSTARS.2014.2355832","volume":"8","author":"Q Wang","year":"2014","unstructured":"Wang Q, Shi W, Atkinson PM, Li Z (2014) Land cover change detection at subpixel resolution with a Hopfield neural network. IEEE J Sel Top Appl Earth Obs Remote Sens 8:1339\u20131352. \n                    https:\/\/doi.org\/10.1109\/JSTARS.2014.2355832","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"380_CR133","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1080\/01431161.2016.1268736","volume":"38","author":"Y Wang","year":"2017","unstructured":"Wang Y, Zhao F, Chen P (2017) A framework of spatiotemporal fuzzy clustering for land-cover change detection using SAR time series. Int J Remote Sens 38:450\u2013466. \n                    https:\/\/doi.org\/10.1080\/01431161.2016.1268736","journal-title":"Int J Remote Sens"},{"key":"380_CR134","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1080\/2150704X.2018.1492172","volume":"9","author":"Q Wang","year":"2018","unstructured":"Wang Q, Zhang X, Chen G, Dai F, Gong Y, Zhu K (2018a) Change detection based on faster R-CNN for high-resolution remote sensing images. Remote Sens Lett 9:923\u2013932. \n                    https:\/\/doi.org\/10.1080\/2150704X.2018.1492172","journal-title":"Remote Sens Lett"},{"key":"380_CR135","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1109\/JSTARS.2018.2810094","volume":"11","author":"X Wang","year":"2018","unstructured":"Wang X, Wang J, Che T, Huang X, Hao X, Li H (2018b) Snow cover mapping for complex mountainous forested environments based on a multi-index technique. IEEE J Sel Top Appl Earth Obs Remote Sens 11:1433\u20131441. \n                    https:\/\/doi.org\/10.1109\/JSTARS.2018.2810094","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"380_CR136","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.isprsjprs.2016.07.003","volume":"119","author":"P Xiao","year":"2016","unstructured":"Xiao P, Zhang X, Wang D, Yuan M, Feng X, Kelly M (2016) Change detection of built-up land\u00a0: a framework of combining pixel-based detection and object-based recognition. ISPRS J Photogramm Remote Sens 119:402\u2013414","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR137","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1080\/01431161.2011.572093","volume":"3","author":"B Xiong","year":"2012","unstructured":"Xiong B, Chen JM, Kuang G (2012) A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images. Remote Sens Lett 3:267\u2013275. \n                    https:\/\/doi.org\/10.1080\/01431161.2011.572093","journal-title":"Remote Sens Lett"},{"key":"380_CR138","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/5032091","volume":"2017","author":"D Xu","year":"2017","unstructured":"Xu D, Chen R, Xing X, Lin W (2017) Detection of decreasing vegetation cover based on empirical orthogonal function and temporal unmixing analysis. Math Probl Eng 2017:1\u201310. \n                    https:\/\/doi.org\/10.1155\/2017\/5032091","journal-title":"Math Probl Eng"},{"key":"380_CR139","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/1353691","volume":"2017","author":"J Xue","year":"2017","unstructured":"Xue J, Su B (2017) Significant remote sensing vegetation indices\u00a0: a review of developments and applications. J Sensors 2017:1\u201317","journal-title":"J Sensors"},{"key":"380_CR140","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs10060841","volume":"10","author":"L Yan","year":"2018","unstructured":"Yan L, Xia W, Zhao Z, Wang Y (2018) A novel approach to unsupervised change detection based on hybrid spectral difference. Remote Sens 10:1\u201321. \n                    https:\/\/doi.org\/10.3390\/rs10060841","journal-title":"Remote Sens"},{"key":"380_CR141","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs9080857","volume":"9","author":"L Yang","year":"2017","unstructured":"Yang L, Jia K, Liang S, Wei X, Yao Y, Zhang X (2017) A robust algorithm for estimating surface fractional vegetation cover from landsat data. Remote Sens 9:1\u201320. \n                    https:\/\/doi.org\/10.3390\/rs9080857","journal-title":"Remote Sens"},{"key":"380_CR142","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2018.01.002","volume":"136","author":"S Ye","year":"2018","unstructured":"Ye S, Rogan J, Sangermano F (2018) Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach. ISPRS J Photogramm Remote Sens 136:134\u2013143","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR143","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","volume":"54","author":"X Yu","year":"2017","unstructured":"Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience Remote Sens 54:741\u2013758. \n                    https:\/\/doi.org\/10.1080\/15481603.2017.1323377","journal-title":"GIScience Remote Sens"},{"key":"380_CR144","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.isprsjprs.2018.08.013","volume":"144","author":"H Yuan","year":"2018","unstructured":"Yuan H, Wu C, Lu L, Wang X (2018) A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index. ISPRS J Photogramm Remote Sens 144:390\u2013399","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR145","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s11069-016-2342-9","volume":"83","author":"A Zanchetta","year":"2016","unstructured":"Zanchetta A, Bitelli G, Karnieli A (2016) Monitoring desertification by remote sensing using the Tasselled cap transform for long-term change detection. Nat Hazards 83:223\u2013237. \n                    https:\/\/doi.org\/10.1007\/s11069-016-2342-9","journal-title":"Nat Hazards"},{"key":"380_CR146","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.isprsjprs.2018.05.021","volume":"142","author":"DC Zanotta","year":"2018","unstructured":"Zanotta DC, Zortea M, Ferreira MP (2018) A supervised approach for simultaneous segmentation and classification of remote sensing images. ISPRS J Photogramm Remote Sens 142:162\u2013173. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2018.05.021","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR147","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","volume":"116","author":"P Zhang","year":"2016","unstructured":"Zhang P, Gong M, Su L, Liu J, Li Z (2016) Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS J Photogramm Remote Sens 116:24\u201341. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.02.013","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR148","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.isprsjprs.2016.02.013","volume":"129","author":"P Zhang","year":"2017","unstructured":"Zhang P, Gong M, Su L, Liu J, Li Z (2017a) Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS J Photogramm Remote Sens 129:212\u2013225. \n                    https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.02.013","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"380_CR149","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.rse.2017.09.022","volume":"201","author":"X Zhang","year":"2017","unstructured":"Zhang X, Xiao P, Feng X, Yuan M (2017b) Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area. Remote Sens Environ 201:243\u2013255. \n                    https:\/\/doi.org\/10.1016\/j.rse.2017.09.022","journal-title":"Remote Sens Environ"},{"key":"380_CR150","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.eja.2017.12.006","volume":"93","author":"B Zhao","year":"2018","unstructured":"Zhao B, Duan A, Ata-ul-karim ST, Liu Z, Chen Z, Gong Z et al (2018) Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. Eur J Agron 93:113\u2013125","journal-title":"Eur J Agron"},{"key":"380_CR151","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.jag.2015.11.002","volume":"45","author":"Z Zheng","year":"2016","unstructured":"Zheng Z, Zeng Y, Li S, Huang W (2016) A new burn severity index based on land surface temperature and enhanced vegetation index. Int J Appl Earth Obs Geoinf 45:84\u201394","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"380_CR152","doi-asserted-by":"publisher","first-page":"4914","DOI":"10.1080\/01431161.2017.1331475","volume":"38","author":"H Zhuang","year":"2017","unstructured":"Zhuang H, Deng K, Yu Y, Fan H (2017) An approach based on discrete wavelet transform to unsupervised change detection in multispectral images. Int J Remote Sens 38:4914\u20134930. \n                    https:\/\/doi.org\/10.1080\/01431161.2017.1331475","journal-title":"Int J Remote Sens"},{"key":"380_CR153","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs10081295","volume":"10","author":"H Zhuang","year":"2018","unstructured":"Zhuang H, Fan H, Deng K, Yao G (2018) A spatial-temporal adaptive neighborhood-based ratio approach for change detection in SAR images. Remote Sens 10:1\u201319. \n                    https:\/\/doi.org\/10.3390\/rs10081295","journal-title":"Remote Sens"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-019-00380-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12145-019-00380-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-019-00380-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,7]],"date-time":"2020-03-07T00:12:03Z","timestamp":1583539923000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12145-019-00380-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,8]]},"references-count":153,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["380"],"URL":"https:\/\/doi.org\/10.1007\/s12145-019-00380-5","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,8]]},"assertion":[{"value":"25 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}