{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:10:11Z","timestamp":1767867011259,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China (NSFC)","award":["41901286, 61971318, 41771377, 41901284"],"award-info":[{"award-number":["41901286, 61971318, 41771377, 41901284"]}]},{"name":"the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources","award":["201906"],"award-info":[{"award-number":["201906"]}]},{"name":"the Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering, CUMT","award":["JS201909"],"award-info":[{"award-number":["JS201909"]}]},{"name":"the PhD Research Startup Fund of East China University of Technology","award":["DHBK2019197"],"award-info":[{"award-number":["DHBK2019197"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results.<\/jats:p>","DOI":"10.3390\/s20051508","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T11:59:36Z","timestamp":1583841576000},"page":"1508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7483-656X","authenticated-orcid":false,"given":"Jinqi","family":"Zhao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglei","family":"Chang","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-4518","authenticated-orcid":false,"given":"Yufen","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Geology Engineering and Geomatics, Chang\u2019an University, Xian 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-1818","authenticated-orcid":false,"given":"Zhong","family":"Lu","sequence":"additional","affiliation":[{"name":"Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingxiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, W., Yang, J., Zhao, J., Shi, H., and Yang, L. (2018). An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3. Sensors, 18.","DOI":"10.3390\/s18020559"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/JSTARS.2012.2201135","article-title":"Multitemporal spaceborne SAR data for urban change detection in China","volume":"5","author":"Ban","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4470","DOI":"10.1109\/TGRS.2019.2891308","article-title":"Back-Projection Tomographic Framework for VHR SAR Image Change Detection","volume":"57","author":"Dominguez","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3773","DOI":"10.1109\/JSTARS.2014.2308273","article-title":"Characteristics analysis and classification of crop harvest patterns by exploiting high-frequency multipolarization SAR data","volume":"7","author":"Zhao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.jhydrol.2019.04.049","article-title":"Soil moisture estimation using two-component decomposition and a hybrid X-Bragg\/Fresnel scattering model","volume":"574","author":"Shi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lu, Z., and Dzurisin, D. (2014). InSAR imaging of Aleutian volcanoes. InSAR Imaging of Aleutian Volcanoes, Springer.","DOI":"10.1007\/978-3-642-00348-6"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, J., Yang, J., Lu, Z., Li, P., Liu, W., and Yang, L. (2017). A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure. Int. J. Remote Sens., 9.","DOI":"10.3390\/rs9080846"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.rse.2014.06.026","article-title":"Seasonal inundation monitoring and vegetation pattern mapping of the Erguna floodplain by means of a RADARSAT-2 fully polarimetric time series","volume":"152","author":"Zhao","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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. Int. J. Remote Sens., 11.","DOI":"10.3390\/rs11050516"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3953","DOI":"10.1109\/TGRS.2016.2532320","article-title":"Polarimetric SAR Change Detection With the Complex Hotelling\u2013Lawley Trace Statistic","volume":"54","author":"Akbari","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.3390\/app7121297","article-title":"An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm","volume":"7","author":"Zhao","year":"2017","journal-title":"Appl. Sci."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"123","article-title":"An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data","volume":"73","author":"Yu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3553","DOI":"10.1109\/TGRS.2013.2273664","article-title":"PolSAR time series processing with binary partition trees","volume":"52","author":"Salembier","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6746","DOI":"10.1109\/TGRS.2016.2590145","article-title":"Region-Based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models","volume":"54","author":"Yang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/LGRS.2011.2167211","article-title":"A neighborhood-based ratio approach for change detection in SAR images","volume":"9","author":"Gong","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/LGRS.2016.2606119","article-title":"Logarithmic Mean-Based Thresholding for SAR Image Change Detection","volume":"13","author":"Sumaiya","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1080\/2150704X.2018.1562256","article-title":"A SAR change detection method based on the consistency of single-pixel difference and neighbourhood difference","volume":"10","author":"Cui","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","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":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2014.10.001","article-title":"Fusing Landsat and SAR time series to detect deforestation in the tropics","volume":"156","author":"Reiche","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TGRS.2005.861007","article-title":"Unsupervised change detection on SAR images using fuzzy hidden Markov chains","volume":"44","author":"Carincotte","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TGRS.2010.2052816","article-title":"Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs","volume":"49","author":"Martinis","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1109\/TGRS.2013.2245900","article-title":"Improving urban change detection from multitemporal SAR images using PCA-NLM","volume":"51","author":"Yousif","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/LGRS.2019.2903596","article-title":"Mapping wetland dynamics with SAR-based change detection in the cloud","volume":"16","author":"Muro","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Canty, M.J., Nielsen, A.A., Conradsen, K., and Skriver, H. (2020). Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine. Int. J. Remote Sens., 12.","DOI":"10.3390\/rs12010046"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.1109\/TGRS.2009.2019269","article-title":"Estimation of the equivalent number of looks in polarimetric synthetic aperture radar imagery","volume":"47","author":"Anfinsen","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TGRS.2002.808066","article-title":"A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data","volume":"41","author":"Conradsen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.isprsjprs.2015.02.008","article-title":"Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series","volume":"107","author":"Atto","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2015.01.007","article-title":"The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation","volume":"102","author":"Schmitt","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Muro, J., Canty, M., Conradsen, K., H\u00fcttich, C., Nielsen, A.A., Skriver, H., Remy, F., Strauch, A., Thonfeld, F., and Menz, G. (2016). Short-term change detection in wetlands using Sentinel-1 time series. Int. J. Remote Sens., 8.","DOI":"10.3390\/rs8100795"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1109\/TGRS.2015.2510160","article-title":"Determining the points of change in time series of polarimetric SAR data","volume":"54","author":"Conradsen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"273","article-title":"A new method for gray-level picture thresholding using the entropy of the histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vision Image Underst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jung, C.H., Song, W.Y., Rho, S.H., Kim, J., Park, J.T., and Kwag, Y.K. (2010, January 10\u201314). Double-step fast CFAR scheme for multiple target detection in high resolution SAR images. Proceedings of IEEE Radar Conference, Washington, DC, USA.","DOI":"10.1109\/RADAR.2010.5494444"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0165-1684(01)00158-X","article-title":"Statistical characterisation and modelling of SAR images","volume":"82","author":"Chitroub","year":"2002","journal-title":"Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1109\/LGRS.2014.2307586","article-title":"SAR image segmentation via hierarchical region merging and edge evolving with generalized gamma distribution","volume":"11","author":"Qin","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","unstructured":"Bucciarelli, T., Lombardo, P., Oliver, C., and Perrotta, M. (1995, January 10\u201314). A compound Weibull model for SAR texture analysis. Proceedings of the International Geoscience and Remote Sensing Symposium, Quantitative Remote Sensing for Science and Applications, Firenze, Italy."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/19475701003648077","article-title":"Characterizing 6 August 2007 Crandall Canyon mine collapse from ALOS PALSAR InSAR","volume":"1","author":"Lu","year":"2010","journal-title":"Geomatics, Natural Hazards and Risk"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/LGRS.2015.2418217","article-title":"Region-based classification of SAR images using Kullback\u2013Leibler distance between generalized gamma distributions","volume":"12","author":"Qin","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1109\/TGRS.2016.2634862","article-title":"Scheme of parameter estimation for generalized gamma distribution and its application to ship detection in SAR images","volume":"55","author":"Gao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric radar imaging: from basics to applications, CRC Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1508\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:05:34Z","timestamp":1760173534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,9]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051508"],"URL":"https:\/\/doi.org\/10.3390\/s20051508","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,9]]}}}