Unsupervised Transformer Boundary Autoencoder Network for Hyperspectral Image Change Detection

Abstract

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods.

A Semi-Supervised Domain Alignment Transformer for Hyperspectral Images Change Detection

“…The low-dimensional features are used for classification or CD. For example, Liu et al [13] used a transformer as the backbone of the autoencoder to extract the low-dimensional features that can be used for CD and edge detection. Hu et al [14] deployed a Siamese autoencoder to detect anomaly change.…”