Unsupervised saliency-guided SAR image change detection. (January 2017)

• Record Type: Journal Article
• Authors: Zheng, Yaoguo; Jiao, Licheng; Liu, Hongying; Zhang, Xiangrong; Hou, Biao; Wang, Shuang
• Is Part Of: Pattern recognition. Volume 61(2017:Jan.)
• Page Start: 309
• Page End: 326
• Publication Date: 2017-01
• Subjects: Unsupervised change detection -- Saliency map -- Principal component analysis -- K-means clustering -- Synthetic aperture radar (SAR) images; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.07.040
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In this paper, a novel unsupervised saliency-guided synthetic aperture radar (SAR) image change detection method is proposed. Salient areas of an image always are discriminative and different from other areas, which make them easily noticed. The strong visual contrast of local areas makes saliency suitable to guide the change detection of SAR images, where exists a difference between the two images. By applying the saliency extraction on an initial difference map obtained via the log ratio operator, a saliency map can be obtained in which most of the changed areas are included and the false changed pixels raised by speckle noises are well neglected, simultaneously. Then, by thresholding the saliency map, most of the interest regions can be preserved and further used to extract regions from the initial SAR images to generate difference image. The principal component analysis (PCA) method is used to extract features from local patches to incorporate the spatial information and reduce the influence of isolated pixels. Finally, k -means clustering is employed to obtain the change map on the extracted features, which are clustered into two classes: changed areas and unchanged areas. Experimental results on five real and two simulated SAR image data sets have demonstrated the effectiveness of the proposed method. Abstract : Highlights: Saliency is used to guide the change detection of SAR images. A framework from localization to recognition is proposed. Experimental results show the well performance of SGK.