Remote Sensing Image Change Detection Based on Deep Multi-Scale Multi-Attention Siamese Transformer Network

Abstract

Change detection is a technique that can observe changes in the surface of the earth dynamically. It is one of the most significant tasks in remote sensing image processing. In the past few years, with the ability of extracting rich deep image features, the deep learning techniques have gained popularity in the field of change detection. In order to obtain obvious image change information, the attention mechanism is added in the decoder and output stage in many deep learning-based methods. Many of these approaches neglect to upgrade the ability of the encoders and the feature extractors to extract the representational features. To resolve this problem, this study proposes a deep multi-scale multi-attention siamese transformer network. A special contextual attention module combining a convolution and self-attention module is introduced into the siamese feature extractor to enhance the global representation ability. A lightly efficient channel attention block is added in the siamese feature extractor to obtain the information interaction among different channels. Furthermore, a multi-scale feature fusion module is proposed to fuse the features from different stages of the siamese feature extractor, and it can detect objects of different sizes and irregularities. To increase the accuracy of the proposed approach, the transformer module is utilized to model the long-range context in two-phase images. The experimental results on the LEVIR-CD and the CCD datasets show the effectiveness of the proposed network.