A Deep Multiscale Pyramid Network Enhanced With Spatial–Spectral Residual Attention for Hyperspectral Image Change Detection

Abstract

Change detection plays an important role in Earth surface observation and has been extensively investigated over recent decades. A hyperspectral image (HSI) with high spectral resolution provides abundant ground object information, which is expected by finer change detection. The existing convolutional neural network (CNN)-based methods extract image features with a fixed kernel, which is incompetent to cope with complicated object details at diverse scales in HSI. In this article, we propose a deep multiscale pyramid network enhanced with spatial–spectral residual attention (DMP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {s}^{2} $ </tex-math></inline-formula> raN) for HSI change detection, which has strong capability to mine multilevel and multiscale spatial–spectral features, improving the performance in complex changed regions. There are two key characteristics: 1) the multiscale spatial–spectral features are extracted by the multiscale pyramid convolution and enhanced by spatial–spectral residual attention module ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {S}^{2} $ </tex-math></inline-formula> RAM) of each scale and 2) the multilevel features are obtained by aggregating the multiscale features level by level. As a result of this design, the proposed DMP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {s}^{2} $ </tex-math></inline-formula> raN learns more discriminative features with both strong semantic information and rich spatial–spectral information. Experiments carried out on three datasets demonstrate the competitive performance of the proposed method in both qualitative and quantitative analyses.