{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:37:51Z","timestamp":1777491471818,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to anthropogenic and natural activities, the land surface continuously changes over time. The accurate and timely detection of changes is greatly important for environmental monitoring, resource management and planning activities. In this study, a novel deep learning-based change detection algorithm is proposed for bi-temporal polarimetric synthetic aperture radar (PolSAR) imagery using a transfer learning (TL) method. In particular, this method has been designed to automatically extract changes by applying three main steps as follows: (1) pre-processing, (2) parallel pseudo-label training sample generation based on a pre-trained model and fuzzy c-means (FCM) clustering algorithm, and (3) classification. Moreover, a new end-to-end three-channel deep neural network, called TCD-Net, has been introduced in this study. TCD-Net can learn more strong and abstract representations for the spatial information of a certain pixel. In addition, by adding an adaptive multi-scale shallow block and an adaptive multi-scale residual block to the TCD-Net architecture, this model with much lower parameters is sensitive to objects of various sizes. Experimental results on two Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) bi-temporal datasets demonstrated the effectiveness of the proposed algorithm compared to other well-known methods with an overall accuracy of 96.71% and a kappa coefficient of 0.82.<\/jats:p>","DOI":"10.3390\/rs14030438","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T22:47:32Z","timestamp":1642546052000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Rezvan","family":"Habibollahi","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3678-4877","authenticated-orcid":false,"given":"Seyd Teymoor","family":"Seydi","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-4475","authenticated-orcid":false,"given":"Mahdi","family":"Hasanlou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7234-959X","authenticated-orcid":false,"given":"Masoud","family":"Mahdianpari","sequence":"additional","affiliation":[{"name":"C-CORE, 1 Morrissey Road, St. John\u2019s, NL A1B 3X5, Canada"},{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1109\/TGRS.2020.2981051","article-title":"A feature difference convolutional neural network-based change detection method","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Hasanlou, M., and Amani, M. (2020). A new end-to-end multi-dimensional CNN framework for land cover\/land use change detection in multi-source remote sensing datasets. Remote Sens., 12.","DOI":"10.3390\/rs12122010"},{"key":"ref_3","first-page":"5200515","article-title":"Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic","volume":"60","author":"Bouhlel","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1109\/TGRS.2020.3011913","article-title":"SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sefrin, O., Riese, F.M., and Keller, S. (2021). Deep Learning for Land Cover Change Detection. Remote Sens., 13.","DOI":"10.3390\/rs13010078"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/LGRS.2020.2993899","article-title":"ShipDeNet-20: An only 20 convolution layers and <1-MB lightweight SAR ship detector","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","first-page":"5210322","article-title":"HOG-ShipCLSNet: A Novel Deep Learning Network with HOG Feature Fusion for SAR Ship Classification","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Jawad, L.A. (2021). Use of multispectral and hyperspectral satellite imagery for monitoring waterbodies and wetlands. Southern Iraq\u2019s Marshes: Their Environment and Conservation, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mohammadimanesh, F., Salehi, B., Mahdianpari, M., Brisco, B., and Gill, E. (2019). Full and simulated compact polarimetry sar responses to canadian wetlands: Separability analysis and classification. Remote Sens., 11.","DOI":"10.3390\/rs11050516"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1080\/15481603.2020.1846948","article-title":"A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: A case study in Newfoundland","volume":"57","author":"Mahdianpari","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/TGRS.2006.888097","article-title":"SAR polarimetry to observe oil spills","volume":"45","author":"Migliaccio","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"63","article-title":"A multifamily GLRT for oil spill detection","volume":"55","author":"Orlando","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Hasanlou, M., and Chanussot, J. (2021). DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images. Remote Sens., 13.","DOI":"10.3390\/rs13245138"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hasanlou, M., Shah-Hosseini, R., Seydi, S.T., Karimzadeh, S., and Matsuoka, M. (2021). Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13061195"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bai, Y., Tang, P., and Hu, C. (2018). kCCA transformation-based radiometric normalization of multi-temporal satellite images. Remote Sens., 10.","DOI":"10.3390\/rs10030432"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cao, C., Dragi\u0107evi\u0107, S., and Li, S. (2019). Land-use change detection with convolutional neural network methods. Environments, 6.","DOI":"10.3390\/environments6020025"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TNNLS.2018.2847309","article-title":"Local restricted convolutional neural network for change detection in polarimetric SAR images","volume":"30","author":"Liu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"De Bem, P.P., de Carvalho Junior, O.A., Fontes Guimar\u00e3es, R., and Trancoso Gomes, R.A. (2020). Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks. Remote Sens., 12.","DOI":"10.3390\/rs12060901"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12145-019-00380-5","article-title":"Change detection techniques for remote sensing applications: A survey","volume":"12","author":"Asokan","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, J.-S., and Pottier, E. (2017). Polarimetric Radar Imaging: From Basics to Applications, CRC Press.","DOI":"10.1201\/9781420054989"},{"key":"ref_22","unstructured":"Verma, R. (2012). Polarimetric Decomposition Based on General Characterisation of Scattering from Urban Areas and Multiple Component Scattering Model. [Master\u2019s Thesis, University of Twente]."},{"key":"ref_23","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press. [2nd ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/TIP.2002.999678","article-title":"An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images","volume":"11","author":"Bruzzone","year":"2002","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TFUZZ.2013.2249072","article-title":"Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images","volume":"22","author":"Gong","year":"2013","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2004.835294","article-title":"On the possibility of automatic multisensor image registration","volume":"42","author":"Inglada","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1080\/014311698215649","article-title":"Speckle filtering in satellite SAR change detection imagery","volume":"19","author":"Dekker","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TGRS.2019.2901945","article-title":"A deep learning method for change detection in synthetic aperture radar images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TGRS.2007.893568","article-title":"A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"963","DOI":"10.5194\/isprs-archives-XLII-4-W18-963-2019","article-title":"Transformation Based Algorithms for Change Detection in Full Polarimetric remote SENSING Images","volume":"42","author":"Seydi","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7029","DOI":"10.1080\/01431161.2018.1466079","article-title":"Hyperspectral change detection: An experimental comparative study","volume":"39","author":"Hasanlou","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0031-3203(86)90030-0","article-title":"Minimum error thresholding","volume":"19","author":"Kittler","year":"1986","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TGRS.2006.876288","article-title":"Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1109\/JSTARS.2014.2344017","article-title":"Unsupervised change detection in multitemporal SAR images over large urban areas","volume":"7","author":"Hu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/01431161.2013.871596","article-title":"Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm","volume":"35","author":"Su","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using combined difference image and $ k $-means clustering for SAR image change detection","volume":"11","author":"Zheng","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/LGRS.2016.2550666","article-title":"SAR image change detection based on multiple kernel K-means clustering with local-neighborhood information","volume":"13","author":"Jia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/LGRS.2015.2484220","article-title":"Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A robust fuzzy local information C-means clustering algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","article-title":"Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering","volume":"21","author":"Gong","year":"2011","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106971","DOI":"10.1016\/j.patcog.2019.106971","article-title":"Stacked Fisher autoencoder for SAR change detection","volume":"96","author":"Liu","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1049\/iet-ipr.2018.6248","article-title":"Change detection in SAR images using deep belief network: A new training approach based on morphological images","volume":"13","author":"Samadi","year":"2019","journal-title":"IET Image Processing"},{"key":"ref_45","first-page":"8005205","article-title":"Change detection in image time-series using unsupervised lstm","volume":"19","author":"Saha","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","unstructured":"Petrou, M., and Sturm, P. (2009). Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution. Iberoamerican Congress on Pattern Recognition, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","article-title":"From W-Net to CDGAN: Bitemporal change detection via deep learning techniques","volume":"58","author":"Hou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jaturapitpornchai, R., Matsuoka, M., Kanemoto, N., Kuzuoka, S., Ito, R., and Nakamura, R. (2019). Newly built construction detection in SAR images using deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11121444"},{"key":"ref_50","first-page":"8004505","article-title":"L-UNet: An LSTM Network for Remote Sensing Image Change Detection","volume":"19","author":"Sun","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1049\/iet-ipr.2018.5172","article-title":"SAR image change detection based on deep denoising and CNN","volume":"13","author":"Cao","year":"2019","journal-title":"IET Image Processing"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, F., and Dong, J. (2021, January 7). Change detection from SAR images based on deformable residual convolutional neural networks. Proceedings of the 2nd ACM International Conference on Multimedia in Asia, Online.","DOI":"10.1145\/3444685.3446320"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"407","DOI":"10.5194\/isprsarchives-XL-1-W5-407-2015","article-title":"Unsupervised Change Detection in SAR images using Gaussian Mixture Models","volume":"40","author":"Kiana","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TNNLS.2016.2636227","article-title":"A deep convolutional coupling network for change detection based on heterogeneous optical and radar images","volume":"29","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bergamasco, L., Saha, S., Bovolo, F., and Bruzzone, L. (2019, January 9\u201311). Unsupervised change-detection based on convolutional-autoencoder feature extraction. Proceedings of the Image and Signal Processing for Remote Sensing XXV, Strasbourg, France.","DOI":"10.1117\/12.2533812"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102585","DOI":"10.1016\/j.jvcir.2019.102585","article-title":"Automatic building change image quality assessment in high resolution remote sensing based on deep learning","volume":"63","author":"Huang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/LGRS.2019.2895656","article-title":"Sea ice change detection in SAR images based on convolutional-wavelet neural networks","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","unstructured":"Zhang, X., Su, H., Zhang, C., Atkinson, P.M., Tan, X., Zeng, X., and Jian, X. (2020). A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2019.2916601","article-title":"Convolutional neural network-based transfer learning for optical aerial images change detection","volume":"17","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"126385","DOI":"10.1109\/ACCESS.2020.3008036","article-title":"Deep learning for change detection in remote sensing images: Comprehensive review and meta-analysis","volume":"8","author":"Khelifi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kutlu, H., and Avc\u0131, E. (2019). A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors, 19.","DOI":"10.3390\/s19091992"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s11220-019-0252-0","article-title":"Sample selection based change detection with dilated network learning in remote sensing images","volume":"20","author":"Venugopal","year":"2019","journal-title":"Sens. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yommy, A.S., Liu, R., and Wu, S. (2015, January 26\u201327). SAR image despeckling using refined Lee filter. Proceedings of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.236"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/JSTARS.2018.2794888","article-title":"A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening","volume":"11","author":"Yuan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and $ k $-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"046019","DOI":"10.1117\/1.JRS.10.046019","article-title":"Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine","volume":"10","author":"Gao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LGRS.2016.2611001","article-title":"Automatic change detection in synthetic aperture radar images based on PCANet","volume":"13","author":"Gao","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1109\/LGRS.2017.2696158","article-title":"Change detection in polarimetric SAR images using a geodesic distance between scattering mechanisms","volume":"14","author":"Ratha","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/TGRS.2018.2866367","article-title":"Detecting changes in fully polarimetric SAR imagery with statistical information theory","volume":"57","author":"Nascimento","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:03:14Z","timestamp":1760133794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":69,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030438"],"URL":"https:\/\/doi.org\/10.3390\/rs14030438","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]}}}