关键词: 极化合成孔径雷达图像, 变化检测, 阈值分割, l1-PCA

Abstract: To improve the robustness and detection accuracy of the polarimetric synthetic aperture radar (SAR) image change detection, we propose a change detection method based on l1-norm principal component analysis (l1-PCA). We use the complex Hotelling-Lawley matrix trace change detection operator to create the difference map. Then, the l1-PCA is used to extract change information from the difference map. Every pixel of the difference map is represented by a feature vector. Finally,the change map is achieved by the k-means classification algorithm.This method is an unsupervised change detection method. Compared with l2-norm principal component analysis (l2-PCA), l1-PCA has higher robustness in feature extraction and can further improve the accuracy of change detection. Experimental results implemented on 3 RADARSAT-2 image datasets illustrate that the proposed method performs better than two typical comparable algorithms in stability and accuracy.

Key words: polarimetric synthetic aperture radar (SAR), change detection, threshold segmentation, l1-norm principal component analysis (l1-PCA)