ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprsarchives-XL-1-W5-407-2015 UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS Kiana E. 1 Homayouni S. 2 Sharifi M. A. 1 Farid-Rohani M. 3 School of Surveying and Geospatial Engineering, Dept. of Remote Sensing, College of Engineering, U. of Tehran, Tehran, Iran Dept. of Geography, Environmental Studies and Geomatics, U. of Ottawa, Ottawa, Canada Faculty of mathematical Sciences, Dept. of Statistics, University of Shahid Beheshti, Tehran, Iran 11 12 2015 XL-1/W5 407 410 Copyright: © 2015 E. Kiana et al. 2015 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://isprs-archives.copernicus.org/articles/XL-1-W5/407/2015/isprs-archives-XL-1-W5-407-2015.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XL-1-W5/407/2015/isprs-archives-XL-1-W5-407-2015.pdf

In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.