{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:16:38Z","timestamp":1777389398470,"version":"3.51.4"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T00:00:00Z","timestamp":1554854400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671369"],"award-info":[{"award-number":["41671369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s12145-019-00383-2","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T22:03:28Z","timestamp":1554933808000},"page":"341-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation"],"prefix":"10.1007","volume":"12","author":[{"given":"Yangyang","family":"Chen","sequence":"first","affiliation":[]},{"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[]},{"given":"Xianwei","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,10]]},"reference":[{"key":"383_CR1","unstructured":"Abadi M et al (2016) Tensorflow: a system for large-scale machine learning. In: Proceedings of 2016 USENIX Symposium on Operating Systems Design and Implementation 16:265\u2013283"},{"key":"383_CR2","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2010) SLIC superpixels. EPFL Technical Report 149300"},{"key":"383_CR3","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"383_CR4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","volume":"130","author":"R Alshehhi","year":"2017","unstructured":"Alshehhi R, Marpu PR, Woon WL, Dalla Mura M (2017) Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 130:139\u2013149","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR5","unstructured":"Audebert N, Le Saux B, Lefevre S (2016) How useful is region-based classification of remote sensing images in a deep learning framework? In: Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. IEEE, pp 5091\u20135094"},{"key":"383_CR6","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3390\/rs9040368","volume":"9","author":"N Audebert","year":"2017","unstructured":"Audebert N, Saux BL, Lef\u00e8vre S (2017) Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. Remote Sens 9:368","journal-title":"Remote Sens"},{"issue":"3","key":"383_CR7","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/JPROC.2012.2237076","volume":"101","author":"Jon Atli Benediktsson","year":"2013","unstructured":"Benediktsson JA, Chanussot J, Moon WM (2013) Advances in very-high-resolution remote sensing. In: Proceedings of the IEEE 101(3):566\u2013569","journal-title":"Proceedings of the IEEE"},{"issue":"1","key":"383_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y. Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Foundations and Trends\u00ae in Maching Learning 2(1):1\u2013127","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"383_CR9","first-page":"12","volume":"6","author":"T Blaschke","year":"2001","unstructured":"Blaschke T (2001) What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT\/GIS 6:12\u201317","journal-title":"GeoBIT\/GIS"},{"key":"383_CR10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","volume":"65","author":"T Blaschke","year":"2010","unstructured":"Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2\u201316","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","volume":"87","author":"T Blaschke","year":"2014","unstructured":"Blaschke T et al (2014) Geographic object-based image analysis\u2013towards a new paradigm. ISPRS J Photogramm Remote Sens 87:180\u2013191","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR12","first-page":"2","volume":"65","author":"T Blaschke","year":"2008","unstructured":"Blaschke T, Lang S, Hay G (2008) Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. IEEE Trans Geosci Remote Sens 65:2\u201316","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR13","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1080\/10106049.2015.1004131","volume":"30","author":"F C\u00e1novas-Garc\u00eda","year":"2015","unstructured":"C\u00e1novas-Garc\u00eda F, Alonso-Sarr\u00eda F (2015a) A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery. Geocarto International 30:937\u2013961","journal-title":"Geocarto International"},{"key":"383_CR14","doi-asserted-by":"crossref","first-page":"4651","DOI":"10.3390\/rs70404651","volume":"7","author":"F C\u00e1novas-Garc\u00eda","year":"2015","unstructured":"C\u00e1novas-Garc\u00eda F, Alonso-Sarr\u00eda F (2015b) Optimal combination of classification algorithms and feature ranking methods for object-based classification of submeter resolution Z\/I-Imaging DMC imagery. Remote Sens 7:4651\u20134677","journal-title":"Remote Sens"},{"key":"383_CR15","unstructured":"Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks. Acta Ecologica Sinica 28(2):627\u2013635"},{"key":"383_CR16","unstructured":"Chavez Jr PS (1984) Digital processing techniques for image mapping with Landsat TM and SPOT simulator data. In: Proceedings of the Eighteenth International Symposium on Remote Sensing of Environment. pp 101\u2013116"},{"key":"383_CR17","first-page":"23","volume":"8","author":"P Chavez","year":"1982","unstructured":"Chavez P, Berlin GL, Sowers LB (1982) Statistical method for selecting landsat. MSS J Appl Photogr Eng 8:23\u201330","journal-title":"MSS J Appl Photogr Eng"},{"key":"383_CR18","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3390\/rs9060570","volume":"9","author":"Q Chen","year":"2017","unstructured":"Chen Q, Li L, Xu Q, Yang S, Shi X, Liu X (2017) Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach. Remote Sens 9:570","journal-title":"Remote Sens"},{"key":"383_CR19","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","volume":"11","author":"X Chen","year":"2014","unstructured":"Chen X, Xiang S, Liu C-L, Pan C-H (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11:1797\u20131801","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"383_CR20","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54:6232\u20136251","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"383_CR21","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","volume":"105","author":"Gong Cheng","year":"2017","unstructured":"Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: benchmark and state of the art. In: Proceedings of the IEEE 105(10):1865\u20131883","journal-title":"Proceedings of the IEEE"},{"key":"383_CR22","doi-asserted-by":"crossref","unstructured":"Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"383_CR23","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.compenvurbsys.2007.10.001","volume":"32","author":"C Cleve","year":"2008","unstructured":"Cleve C, Kelly M, Kearns FR, Moritz M (2008) Classification of the wildland\u2013urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography Computers. Environment and Urban Systems 32:317\u2013326","journal-title":"Environment and Urban Systems"},{"key":"383_CR24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/rs9030243","volume":"9","author":"O Csillik","year":"2017","unstructured":"Csillik O (2017) Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens 9:243","journal-title":"Remote Sens"},{"key":"383_CR25","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","volume":"20","author":"GE Dahl","year":"2012","unstructured":"Dahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio Speech Lang Process 20:30\u201342","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"383_CR26","doi-asserted-by":"crossref","unstructured":"Darwish A, Leukert K, Reinhardt W (2003) Image segmentation for the purpose of object-based classification. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International. IEEE, pp 2039\u20132041","DOI":"10.1109\/IGARSS.2003.1294332"},{"key":"383_CR27","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"383_CR28","doi-asserted-by":"crossref","unstructured":"Djerriri K, Karoui MS (2017) Classification of quickbird imagery over urban area using convolutional neural network. In: Urban Remote Sensing Event (JURSE), 2017 Joint. IEEE, pp 1\u20134","DOI":"10.1109\/JURSE.2017.7924631"},{"key":"383_CR29","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","volume":"24","author":"L Dr\u01cegu\u0163","year":"2010","unstructured":"Dr\u01cegu\u0163 L, Tiede D, Levick SR (2010) ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci 24:859\u2013871","journal-title":"Int J Geogr Inf Sci"},{"key":"383_CR30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303\u2013338","journal-title":"Int J Comput Vis"},{"key":"383_CR31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.patcog.2011.03.035","volume":"45","author":"M Fauvel","year":"2012","unstructured":"Fauvel M, Chanussot J, Benediktsson JA (2012) A spatial\u2013spectral kernel-based approach for the classification of remote-sensing images. Pattern Recogn 45:381\u2013392","journal-title":"Pattern Recogn"},{"key":"383_CR32","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59:167\u2013181","journal-title":"Int J Comput Vis"},{"key":"383_CR33","doi-asserted-by":"crossref","first-page":"498","DOI":"10.3390\/rs9050498","volume":"9","author":"G Fu","year":"2017","unstructured":"Fu G, Liu C, Zhou R, Sun T, Zhang Q (2017) Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens 9:498","journal-title":"Remote Sens"},{"key":"383_CR34","doi-asserted-by":"crossref","unstructured":"Fukushima K, Miyake S (1982) Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and cooperation in neural nets. Springer, pp 267\u2013285","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"383_CR35","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"383_CR36","volume-title":"Digital image processing","author":"RC Gonzalez","year":"1992","unstructured":"Gonzalez RC, Woods RE (1992) Digital image processing. Addison-wesley, Boston"},{"key":"383_CR37","doi-asserted-by":"crossref","unstructured":"Gonzalo-Martin C, Garcia-Pedrero A, Lillo-Saavedra M, Menasalvas E (2016) Deep learning for superpixel-based classification of remote sensing images. In: GEOBIA 2016","DOI":"10.3990\/2.401"},{"key":"383_CR38","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s10844-015-0365-4","volume":"46","author":"C Gonzalo-Mart\u00edn","year":"2016","unstructured":"Gonzalo-Mart\u00edn C, Lillo-Saavedra M, Menasalvas E, Fonseca-Luengo D, Garc\u00eda-Pedrero A, Costumero R (2016) Local optimal scale in a hierarchical segmentation method for satellite images. J Intell Inf Syst 46:517\u2013529","journal-title":"J Intell Inf Syst"},{"key":"383_CR39","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"383_CR40","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jag.2005.06.005","volume":"7","author":"GJ Hay","year":"2005","unstructured":"Hay GJ, Castilla G, Wulder MA, Ruiz JR (2005) An automated object-based approach for the multiscale image segmentation of forest scenes. Int J Appl Earth Obs Geoinf 7:339\u2013359","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"383_CR41","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G et al (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 29:82\u201397","journal-title":"IEEE Signal Process Mag"},{"key":"383_CR42","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","volume":"7","author":"F Hu","year":"2015","unstructured":"Hu F, Xia G-S, Hu J, Zhang L (2015) Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 7:14680\u201314707","journal-title":"Remote Sens"},{"key":"383_CR43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","volume":"80","author":"M Hussain","year":"2013","unstructured":"Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91\u2013106","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR44","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ecoinf.2010.02.004","volume":"5","author":"JW Karl","year":"2010","unstructured":"Karl JW, Maurer BA (2010) Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information. Ecological Informatics 5:194\u2013202","journal-title":"Ecological Informatics"},{"key":"383_CR45","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"383_CR46","doi-asserted-by":"crossref","first-page":"035016","DOI":"10.1117\/1.JRS.11.035016","volume":"11","author":"T Kavzoglu","year":"2017","unstructured":"Kavzoglu T, Erdemir MY, Tonbul H (2017) Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach. J Appl Remote Sens 11:035016","journal-title":"J Appl Remote Sens"},{"key":"383_CR47","doi-asserted-by":"crossref","unstructured":"Kavzoglu T, Tonbul H (2017a) A Comparative study of segmentation quality for multi-resolution segmentation and watershed transform. In: Recent Advances in Space Technologies (RAST), 2017 8th International Conference on. IEEE, pp 113\u2013117","DOI":"10.1109\/RAST.2017.8002984"},{"key":"383_CR48","unstructured":"Kavzoglu T, Tonbul H (2017b) Selecting optimal slic superpixels parameters by using discrepancy measures. In: Asian Conference On Remote Sensing"},{"key":"383_CR49","doi-asserted-by":"crossref","unstructured":"Kavzoglu T, Tonbul H (2018) An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery. Int J Remote Sens:1\u201317","DOI":"10.1080\/01431161.2018.1506592"},{"key":"383_CR50","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images"},{"key":"383_CR51","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097\u20131105"},{"key":"383_CR52","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3390\/rs8040329","volume":"8","author":"M L\u00e4ngkvist","year":"2016","unstructured":"L\u00e4ngkvist M, Kiselev A, Alirezaie M, Loutfi A (2016) Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sens 8:329","journal-title":"Remote Sens"},{"key":"383_CR53","first-page":"1","volume":"10","author":"H Larochelle","year":"2009","unstructured":"Larochelle H, Bengio Y, Louradour J, Lamblin P (2009) Exploring strategies for training deep neural networks. J Mach Learn Res 10:1\u201340","journal-title":"J Mach Learn Res"},{"key":"383_CR54","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541\u2013551","journal-title":"Neural Comput"},{"issue":"11","key":"383_CR55","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y. Lecun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of IEEE 86(11):2278\u20132324","journal-title":"Proceedings of the IEEE"},{"key":"383_CR56","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"383_CR57","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","volume":"31","author":"A Levinshtein","year":"2009","unstructured":"Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K (2009) Turbopixels: Fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 31:2290\u20132297","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"383_CR58","doi-asserted-by":"crossref","first-page":"494","DOI":"10.3390\/rs9050494","volume":"9","author":"F Li","year":"2017","unstructured":"Li F, Li S, Zhu C, Lan X, Chang H (2017a) Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images. Remote Sens 9:494","journal-title":"Remote Sens"},{"key":"383_CR59","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/rs9010022","volume":"9","author":"W Li","year":"2016","unstructured":"Li W, Fu H, Yu L, Cracknell A (2016) Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens 9:22","journal-title":"Remote Sens"},{"key":"383_CR60","doi-asserted-by":"crossref","first-page":"11372","DOI":"10.3390\/rs61111372","volume":"6","author":"X Li","year":"2014","unstructured":"Li X, Shao G (2014) Object-based land-cover mapping with high resolution aerial photography at a county scale in midwestern USA. Remote Sens 6:11372\u201311390","journal-title":"Remote Sens"},{"issue":"1","key":"383_CR61","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/rs9010067","volume":"9","author":"Ying Li","year":"2017","unstructured":"Li Y, Zhang H, Shen Q (2017b) Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens 9:\u201367","journal-title":"Remote Sensing"},{"key":"383_CR62","doi-asserted-by":"crossref","first-page":"480","DOI":"10.3390\/rs9050480","volume":"9","author":"H Lin","year":"2017","unstructured":"Lin H, Shi Z, Zou Z (2017) Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network. Remote Sens 9:480","journal-title":"Remote Sens"},{"key":"383_CR63","unstructured":"Liu M-Y, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 2097\u20132104"},{"key":"383_CR64","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang M-H, Xu P, Guo Z-W (2017) SAR ship detection using sea-land segmentation-based convolutional neural network. In: 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), pp 1\u20134","DOI":"10.1109\/RSIP.2017.7958806"},{"key":"383_CR65","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"383_CR66","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","volume":"55","author":"Y Long","year":"2017","unstructured":"Long Y, Gong Y, Xiao Z, Liu Q (2017) Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks. IEEE Trans Geosci Remote Sens 55:2486\u20132498","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR67","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/01431160701311309","volume":"29","author":"V Lucieer","year":"2008","unstructured":"Lucieer V (2008) Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. Int J Remote Sens 29:905\u2013921","journal-title":"Int J Remote Sens"},{"key":"383_CR68","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1109\/LGRS.2015.2483680","volume":"12","author":"FP Luus","year":"2015","unstructured":"Luus FP, Salmon BP, van den Bergh F, Maharaj BTJ (2015) Multiview deep learning for land-use classification. IEEE Geosci Remote Sens Lett 12:2448\u20132452","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"383_CR69","doi-asserted-by":"crossref","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016) Fully convolutional neural networks for remote sensing image classification. In: Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. IEEE, pp 5071\u20135074","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"383_CR70","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","volume":"55","author":"E Maggiori","year":"2017","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55:645\u2013657","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR71","doi-asserted-by":"crossref","unstructured":"Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International. IEEE, pp 4959\u20134962","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"383_CR72","doi-asserted-by":"crossref","first-page":"042614","DOI":"10.1117\/1.JRS.11.042614","volume":"11","author":"RA Marcum","year":"2017","unstructured":"Marcum RA, Davis CH, Scott GJ, Nivin TW (2017) Rapid broad area search and detection of Chinese surface-to-air missile sites using deep convolutional neural networks. J Appl Remote Sens 11:042614","journal-title":"J Appl Remote Sens"},{"key":"383_CR73","doi-asserted-by":"crossref","unstructured":"Mash R, Borghetti B, Pecarina J (2016) Improved Aircraft Recognition for Aerial Refueling Through Data Augmentation in Convolutional Neural Networks. In: International Symposium on Visual Computing. Springer, pp 113\u2013122","DOI":"10.1007\/978-3-319-50835-1_11"},{"key":"383_CR74","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/LGRS.2011.2182604","volume":"9","author":"D Ming","year":"2012","unstructured":"Ming D, Ci T, Cai H, Li L, Qiao C, Du J (2012) Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geosci Remote Sens Lett 9:813\u2013817","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"383_CR75","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2015.04.010","volume":"106","author":"D Ming","year":"2015","unstructured":"Ming D, Li J, Wang J, Zhang M (2015) Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example. ISPRS J Photogramm Remote Sens 106:28\u201341","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR76","doi-asserted-by":"crossref","unstructured":"Mori G (2005) Guiding model search using segmentation. In: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on. IEEE, pp 1417\u20131423","DOI":"10.1109\/ICCV.2005.112"},{"key":"383_CR77","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","volume":"101","author":"G Moser","year":"2013","unstructured":"Moser G, Serpico SB, Benediktsson JA (2013) Land-cover mapping by Markov modeling of spatial\u2013contextual information in very-high-resolution remote sensing images. Proc IEEE 101:631\u2013651","journal-title":"Proc IEEE"},{"key":"383_CR78","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","volume":"115","author":"SW Myint","year":"2011","unstructured":"Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115:1145\u20131161","journal-title":"Remote Sensing of Environment"},{"key":"383_CR79","unstructured":"Neubert P, Protzel P (2012) Superpixel benchmark and comparison. In: Proc. Forum Bildverarbeitung. pp 1\u201312"},{"key":"383_CR80","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","volume":"61","author":"K Nogueira","year":"2017","unstructured":"Nogueira K, Penatti OA, dos Santos JA (2017) Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recogn 61:539\u2013556","journal-title":"Pattern Recogn"},{"key":"383_CR81","doi-asserted-by":"crossref","unstructured":"Pan X, Zhao J, Xu J (2019) An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classification. Int J Remote Sens:1\u201325","DOI":"10.1080\/01431161.2019.1584687"},{"key":"383_CR82","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1080\/01431160110076162","volume":"23","author":"A Pekkarinen","year":"2002","unstructured":"Pekkarinen A (2002) A method for the segmentation of very high spatial resolution images of forested landscapes. Int J Remote Sens 23:2817\u20132836","journal-title":"Int J Remote Sens"},{"key":"383_CR83","unstructured":"Penatti OA, Nogueira K, dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp 44\u201351"},{"key":"383_CR84","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TGRS.2002.802494","volume":"40","author":"A Plaza","year":"2002","unstructured":"Plaza A, Mart\u00ednez P, P\u00e9rez R, Plaza J (2002) Spatial\/spectral endmember extraction by multidimensional morphological operations. IEEE Trans Geosci Remote Sens 40:2025\u20132041","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR85","doi-asserted-by":"crossref","unstructured":"Ren X, Malik J (2003) Learning a classification model for segmentation. In: IEEE International Conference on Computer Vision, pp 10\u201317","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"383_CR86","unstructured":"Salberg A-B (2015) Detection of seals in remote sensing images using features extracted from deep convolutional neural networks. In: IGARSS. pp 1893\u20131896"},{"key":"383_CR87","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","volume":"14","author":"GJ Scott","year":"2017","unstructured":"Scott GJ, England MR, Starms WA, Marcum RA, Davis CH (2017) Training Deep Convolutional Neural Networks for Land\u2013Cover Classification of High-Resolution Imagery. IEEE Geosci Remote Sens Lett 14:549\u2013553","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"383_CR88","doi-asserted-by":"crossref","unstructured":"Sertel E, Kaya S, Curran P (2007) The use of geostatistical methods to identify severe earthquake damage in an urban area. In: Urban Remote Sensing Joint Event, 2007. IEEE, pp 1\u20135","DOI":"10.1109\/URS.2007.371864"},{"key":"383_CR89","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1080\/01431161.2011.608740","volume":"33","author":"G Sheng","year":"2012","unstructured":"Sheng G, Yang W, Xu T, Sun H (2012) High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int J Remote Sens 33:2395\u20132412","journal-title":"Int J Remote Sens"},{"key":"383_CR90","first-page":"042617","volume":"11","author":"X Sun","year":"2017","unstructured":"Sun X, Shen S, Lin X, Hu Z (2017) Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks. J Appl Remote Sens 11:042617","journal-title":"J Appl Remote Sens"},{"key":"383_CR91","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TGRS.2014.2335751","volume":"53","author":"J Tang","year":"2015","unstructured":"Tang J, Deng C, Huang G-B, Zhao B (2015) Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans Geosci Remote Sens 53:1174\u20131185","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR92","doi-asserted-by":"crossref","unstructured":"Van den Bergh M, Boix X, Roig G, de Capitani B, Van Gool L (2012) SEEDS: Superpixels extracted via energy-driven sampling. In: European conference on computer vision. Springer, pp 13\u201326","DOI":"10.1007\/978-3-642-33786-4_2"},{"key":"383_CR93","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s11263-014-0744-2","volume":"111","author":"M Bergh Van den","year":"2015","unstructured":"Van den Bergh M, Boix X, Roig G, Van Gool L (2015) Seeds: Superpixels extracted via energy-driven sampling. Int J Comput Vis 111:298\u2013314","journal-title":"Int J Comput Vis"},{"key":"383_CR94","first-page":"705","volume":"2008","author":"A Vedaldi","year":"2008","unstructured":"Vedaldi A, Soatto S (2008) Quick shift and kernel methods for mode seeking Computer vision. ECCV 2008:705\u2013718","journal-title":"ECCV"},{"key":"383_CR95","doi-asserted-by":"crossref","first-page":"446","DOI":"10.3390\/rs9050446","volume":"9","author":"H Wang","year":"2017","unstructured":"Wang H, Wang Y, Zhang Q, Xiang S, Pan C (2017) Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images. Remote Sens 9:446","journal-title":"Remote Sens"},{"key":"383_CR96","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang J, Hu X, Wang Y (2016) Automatic detection and classification of oil tanks in optical satellite images based on convolutional neural network. In: International Conference on Image and Signal Processing. Springer, pp 304\u2013313","DOI":"10.1007\/978-3-319-33618-3_31"},{"key":"383_CR97","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/JSTARS.2013.2269996","volume":"7","author":"J Wei","year":"2014","unstructured":"Wei J, Li P, Yang J, Zhang J, Lang F (2014) A New Automatic Ship Detection Method Using $ L $-Band Polarimetric SAR Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7:1383\u20131393","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"383_CR98","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/LGRS.2017.2712638","volume":"14","author":"Z Xiao","year":"2017","unstructured":"Xiao Z, Gong Y, Long Y, Li D, Wang X, Liu H (2017a) Airport detection based on a multiscale fusion feature for optical remote sensing images. IEEE Geosci Remote Sens Lett 14:1469\u20131473","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"383_CR99","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/01431161.2014.999881","volume":"36","author":"Z Xiao","year":"2015","unstructured":"Xiao Z, Liu Q, Tang G, Zhai X (2015) Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images. Int J Remote Sens 36:618\u2013644","journal-title":"Int J Remote Sens"},{"key":"383_CR100","doi-asserted-by":"crossref","first-page":"725","DOI":"10.3390\/rs9070725","volume":"9","author":"Z Xiao","year":"2017","unstructured":"Xiao Z, Long Y, Li D, Wei C, Tang G, Liu J (2017b) High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective. Remote Sens 9:725","journal-title":"Remote Sens"},{"key":"383_CR101","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"key":"383_CR102","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320\u20133328"},{"key":"383_CR103","doi-asserted-by":"crossref","first-page":"799","DOI":"10.14358\/PERS.72.7.799","volume":"72","author":"Q Yu","year":"2006","unstructured":"Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm Eng Remote Sens 72:799\u2013811","journal-title":"Photogramm Eng Remote Sens"},{"key":"383_CR104","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","volume":"6","author":"J Yue","year":"2015","unstructured":"Yue J, Zhao W, Mao S, Liu H (2015) Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters 6:468\u2013477","journal-title":"Remote Sensing Letters"},{"key":"383_CR105","doi-asserted-by":"crossref","first-page":"5553","DOI":"10.1109\/TGRS.2016.2569141","volume":"54","author":"F Zhang","year":"2016","unstructured":"Zhang F, Du B, Zhang L, Xu M (2016) Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Trans Geosci Remote Sens 54:5553\u20135563","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"383_CR106","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/LGRS.2017.2673118","volume":"14","author":"Peng Zhang","year":"2017","unstructured":"Zhang P, Niu X, Dou Y, Xia F (2017a) Airport detection on optical satellite images using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 14(8):1183\u20131187","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"383_CR107","doi-asserted-by":"crossref","first-page":"482","DOI":"10.3390\/rs9050482","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Wu K, Du B, Zhang L, Hu X (2017b) Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information. Remote Sens 9:482","journal-title":"Remote Sens"},{"key":"383_CR108","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","volume":"113","author":"W Zhao","year":"2016","unstructured":"Zhao W, Du S (2016a) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155\u2013165","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"383_CR109","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","volume":"54","author":"W Zhao","year":"2016","unstructured":"Zhao W, Du S (2016b) Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54:4544\u20134554","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"383_CR110","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/JSTARS.2017.2680324","volume":"10","author":"Wenzhi Zhao","year":"2017","unstructured":"Zhao W, Du S, Emery WJ (2017a) Object-based convolutional neural network for high-resolution imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(7):3386\u20133396","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"383_CR111","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/TGRS.2017.2689018","volume":"55","author":"W Zhao","year":"2017","unstructured":"Zhao W et al (2017b) Superpixel-based multiple local CNN for panchromatic and multispectral image classification. IEEE Trans Geosci Remote Sens 55:4141\u20134156","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"383_CR112","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1016\/j.rse.2009.04.007","volume":"113","author":"W Zhou","year":"2009","unstructured":"Zhou W, Huang G, Troy A, Cadenasso M (2009) Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study. Remote Sens Environ 113:1769\u20131777","journal-title":"Remote Sens Environ"},{"key":"383_CR113","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","volume":"12","author":"Q Zou","year":"2015","unstructured":"Zou Q, Ni L, Zhang T, Wang Q (2015) Deep learning based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 12:2321\u20132325","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-019-00383-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12145-019-00383-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-019-00383-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T23:07:49Z","timestamp":1586387269000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12145-019-00383-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,10]]},"references-count":113,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["383"],"URL":"https:\/\/doi.org\/10.1007\/s12145-019-00383-2","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,10]]},"assertion":[{"value":"28 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}