An Efficient Visual State Space Model for Remote Sensing Binary Change Detection

Abstract

Transformer and convolutional neural network (CNN) have made significant progress in the issue of remote sensing binary change detection. However, Transformer has high quadratic computational complexity, while CNN is limited by a fixed receptive field, which may hinder their capability of learning spatial contextual features. Inspired by the remarkable performance of Mamba on the task of natural language processing, which can effectively make up for the deficiencies of the above two architectures, we tailor the structure of Mamba to solve the issue of binary change detection. In this work, we explore the potential of visual Mamba to address the task of binary change detection in remote sensing imageries, which is abbreviated as Mam-BCD. The entire network is designed as an encoder–decoder architecture. The encoder employs the effective visual Mamba to fully learn global spatial contextual features from input images. For the decoder, we introduce three spatio-temporal feature learning strategies, which can be organically integrated into the Mamba architecture to achieve spatio-temporal interaction between different temporal features. Comprehensive experiments are conducted on three public available datasets to verify the efficacy of the proposed Mam-BCD. Compared to the advanced CTDFormer, Mam-BCD achieves 4.49%, 8.73% and 3.44% gain in accuracy metric on SYSU-CD, LEVIR-CD+ and WHU-CD datasets, respectively.