Change Detection of Multi‐temporal Remote Sensing Images Based on Contourlet Transform and Ica

Abstract

In order to improve the accuracy and computational efficiency of change detection of multi‐temporal remote sensing images, a change detection algorithm based on contourlet transform and independent component analysis (ICA) is proposed. Firstly, multi‐scale decomposition of image data is performed by using contourlet transform with multi‐scale, directionality and anisotropy. Then independent component analysis is carried out for the decomposed data. And the independent data components are separated by the improved fixed point ICA algorithm based on Newton iteration. Next the separated data components are transformed into image components. Finally, change detection is achieved by threshold segmentation and filtering for change image components. The experimental results show that, compared with the existing three change detection algorithms such as the algorithm based on PCA, the algorithm based on ICA and the algorithm based on wavelet transform and ICA, the proposed algorithm in this paper more effectively separates change information and reduces computational complexity. The obtained change image has higher accuracy and strong robustness with respect to the background.