Spectral-spatial sequence characteristics-based convolutional transformer for hyperspectral change detection

Recently, ground coverings change detection (CD) driven by bitemporal hyperspectral images (HSIs) has become a hot topic in the remote sensing community. There are two challenges in the HSI-CD task: (1) attribute feature representation of pixel pairs and (2) feature extraction of attribute patterns of pixel pairs. To solve the above problems, a novel spectral-spatial sequence characteristics-based convolutional transformer (S3C-CT) method is proposed for the HSI-CD task. In the designed method, firstly, an eigenvalue extrema-based band selection strategy is introduced to pick up spectral information with salient attribute patterns. Then, a 3D tensor with spectral-spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs. Next, a fusion framework of the convolutional neural network (CNN) and Transformer encoder (TE) is designed to extract high-order sequence semantic features, taking into account both local context information and global sequence dependencies. Specifically, a spatial-spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE. Finally, the binary change map is determined according to the fully-connected layer. Experimental results on real HSI datasets indicated that the proposed S3C-CT method outperforms other well-known and state-of-the-art detection approaches in terms of detection performance.