CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection

Change detection (CD) aims to detect the changing area of the same scene at different times, which is an important application of remote sensing images. As the key data source of CD, hyperspectral image (HSI) is widely used in CD technology because of its rich spectral–spatial information. However, how to mine the multilevel spatial information of dual-temporal HSIs and focus on the features of the pixels to be classified individually remains a problem in the spatial attention mechanism (SAM). To make full use of the spectral–spatial information of HSIs, in this article we propose a CNN framework with compact band weighting and multiscale spatial attention (CBW-MSSANet) for HSI pixel-level CD. The main contributions of this article are as follows: 1) a new method of pseudolabel training sample selection based on
$k$
-means (KM) centroid distance is designed; 2) apply the CBW module to HSI CD to take full advantage of the spectral information of HSIs; 3) an MSSA module is developed for the pixel-level CD, which can mine multilevel spatial information and pay more attention to the features of the pixels to be classified and combine the spatial information of adjacent pixels to make it more conducive to pixel-level CD. Experimental results on four real-HSI datasets demonstrated that the performance of MSSA surpasses the classical single-scale SAM, and CBW-MSSANet is superior to some representative CD methods.