BRCN-ERN: A Bidirectional Reconstruction Coding Network and Enhanced Residual Network for Hyperspectral Change Detection

Abstract

Change detection (CD) is a hot issue in the field of remote sensing. Hyperspectral images (HSI) contain rich spectral information, and have gradually become an important data source in CD. Spectral-spatial combination is a commonly used strategy for suppressing the influence of noise on the spectrum. However, it is difficult to find a feature space that allows both spectral and spatial features to be optimally expressed. Therefore, this letter proposes a bidirectional reconstruction coding network and enhanced residual network for HSI CD (i.e., BRCN-ERN) based on the strategy of completely extracting spectral and spatial features separately and then fusing them together. In the spectral module, we use the spectrum of unchanged pixels at two time points to construct a bidirectional reconstruction network, and use the reconstruction error as a new source of spectral features. In the spatial module, we use advanced band selection algorithms to filter the bands with good spatial information, and design an enhanced two-dimensional residual network to extract the spatial features of the change tensor. Finally, the obtained spectral and spatial feature vectors are fused and inputted into the fully connected classification network to obtain the final CD map. Real HSI experiments show that our proposed BRCN-ERN has a better CD effect and is more effective than most existing algorithms.