A Valuable approach for Image Processing and Change Detection on Synthetic Aperture Radar Data

In this paper, we proposed an approach for unsupervised change detection technique on SAR data. Change detection is process of automatically identifying and analyzing the regions which undergone some changes such as spatial or spectral changes. As various traditional techniques are available to detect change on satellite images. In order to detect change on SAR images we use PCA technique which involve Singular Value Decomposition Method (SVD) method to process the images. After that we compare the images pixel by pixel and find out the changed pixels and map those pixels to display the changed map.

[1]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[2]  William J. Emery,et al.  An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Tamás Szirányi,et al.  Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[5]  Francesca Bovolo,et al.  A Multilevel Parcel-Based Approach to Change Detection in Very High Resolution Multitemporal Images , 2005, IEEE Geoscience and Remote Sensing Letters.

[6]  Fabio Del Frate,et al.  Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks , 2010, IEEE Geoscience and Remote Sensing Letters.

[7]  P. Zimmermann.  Automatic analysis , 2000 .

[8]  Turgay Çelik,et al.  Change Detection in Satellite Images Using a Genetic Algorithm Approach , 2010, IEEE Geoscience and Remote Sensing Letters.

[9]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.