Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 407-410, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-407-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Dec 2015
UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS
E. Kiana1, S. Homayouni2, M. A. Sharifi1, and M. Farid-Rohani3 1School of Surveying and Geospatial Engineering, Dept. of Remote Sensing, College of Engineering, U. of Tehran, Tehran, Iran
2Dept. of Geography, Environmental Studies and Geomatics, U. of Ottawa, Ottawa, Canada
3Faculty of mathematical Sciences, Dept. of Statistics, University of Shahid Beheshti, Tehran, Iran
Keywords: Gaussian Mixture model, Change Detection, SAR images, Difference image, Multi-temporal Images Abstract. 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.
Conference paper (PDF, 1277 KB)


Citation: Kiana, E., Homayouni, S., Sharifi, M. A., and Farid-Rohani, M.: UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 407-410, https://doi.org/10.5194/isprsarchives-XL-1-W5-407-2015, 2015.

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