ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model

By Hongruixuan Chen, Jian Song, Chengxi Han, Junshi Xia, Naoto Yokoya. IEEE Transactions on Geoscience and Remote Sensing, 2024

431 citations recorded by the source corpus.

Convolutional neural networks (CNNs) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN is constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models (SSMs), has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this article, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary CD (BCD), semantic CD (SCD), and building damage assessment (BDA), respectively. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images. For the change decoder, which is available in all three architectures, we propose three spatiotemporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatiotemporal interaction of multitemporal features, thereby obtaining accurate change information. On five benchmark datasets, our proposed frameworks outperform current CNN- and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks. Further experiments show that our architecture is quite robust to degraded data. The source code is available at: https://github.com/ChenHongruixuan/MambaCD.

Subfields: Media Technology

View Paper: https://www.semanticscholar.org/paper/541bcf94bd09f17c25d8ed7cd10e69c8f91331d0

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In Media Technology

1. Wanli Ouyang
2. Lei Bai
3. Sijie Zhao
4. Hao Chen
5. Pengfeng Xiao
6. Xue-liang Zhang

Citations in the corpus

• RS-Mamba for Large Remote Sensing Image Dense Prediction (2024, background): “Recent works [14], [15], [35] have achieved global modeling of the context of images using SSM by conducting selective scanning both forward and backward in certain directions.”