Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting

Iteratively reweighted multivariate alteration detection algorithm has the phenomena of broken patches, much noise, and small change area that are difficult to detect, and the overall detection rate is low. In order to solve this problem, this paper proposes a multi-spectral image change detection algorithm based on band selection and single-band iterative weighting. Because the change information of the multi-spectral image is concentrated in some bands, the background and noise information of the rest bands are more, which may have a negative effect on the final result. Therefore, the band with more change information is selected first, and the iterative weighting of a single band can better suppress the noise and background information, so as to obtain a higher band correlation and facilitate the extraction of change information. This method is used to obtain the characteristic difference graph of the selected band with more change information. After Gaussian denoising of each characteristic difference graph, the Euclidean distance formula is used to fuse the difference graph of each band into a change intensity graph. Finally, the unsupervised $k$ -means clustering algorithm is used to perform binary-valued clustering on the fused difference graph to obtain the change detection results. As a practical application, the superior performance of our proposed method was demonstrated through a large number of comparative tests.