Change Detection and Analysis of High Resolution Synthetic Aperture Radar Images Based on Bag-of-words Model

This paper discusses the change detection in high-resolution SAR image interpretation. Referring to the unfavorable elements in the change detection and the status quo, this paper focuses on resolving the semantic information deficiency problem in SAR image change detection. A method named change detection base on Bag of Words Model (BoWM) is proposed. By using the BoWM, two visual histograms of two different temporal images are obtained, and the histogram difference, which contains semantic information, is defined as the change vector. By analyzing the change vector and combining it with the statistical change detection method, the semantic analysis and interest change-type detection of the change area can be obtained. Experiments show that the proposed method may be applicable to the semantic analysis of the change area in high-resolution SAR images.