A Mamba-based Siamese Network for Remote Sensing Change Detection (2407.06839v1)

Abstract: Change detection in remote sensing images is an essential tool for analyzing a region at different times. It finds varied applications in monitoring environmental changes, man-made changes as well as corresponding decision-making and prediction of future trends. Deep learning methods like Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in detecting significant changes, given two images at different times. In this paper, we propose a Mamba-based Change Detector (M-CD) that segments out the regions of interest even better. Mamba-based architectures demonstrate linear-time training capabilities and an improved receptive field over transformers. Our experiments on four widely used change detection datasets demonstrate significant improvements over existing state-of-the-art (SOTA) methods. Our code and pre-trained models are available at https://github.com/JayParanjape/M-CD

• The paper presents a novel Mamba-based Siamese network (M-CD) that significantly improves change detection accuracy on multiple remote sensing datasets.
• It employs specialized modules—Siamese Image Encoder, Difference Module, and Mask Decoder—to capture long-range dependencies and multi-scale differences effectively.
• The approach demonstrates superior performance over existing methods, paving the way for efficient applications in urban development, disaster management, and environmental monitoring.

A Mamba-based Siamese Network for Remote Sensing Change Detection

The paper "A Mamba-based Siamese Network for Remote Sensing Change Detection" presents a novel deep learning approach for the task of change detection (CD) in remote sensing images. CD is a critical task in remote sensing, utilized for monitoring environmental variations, urban development, disaster management, and various military applications. In the proposed approach, the authors introduce a Mamba-based architecture named M-CD, which demonstrates superior performance over existing state-of-the-art (SOTA) methods on multiple datasets.

Methodology and Contributions

The core contributions of this paper are centered around the development of a Mamba-based architecture tailored for change detection, departing from traditional CNNs and transformer-based models. The primary components of the proposed M-CD architecture include:

1. Siamese Image Encoder (SIE): This encoder utilizes the Mamba-based architecture for feature extraction from a pair of pre-change and post-change images. The SIE employs a series of Visual State Space (VSS) blocks, which are adept at capturing long-range dependencies across images through the selective state modeling mechanism. The encoder processes the images separately but shares weights to ensure consistency and reduce computational load.
2. Difference Module (DM): The DM is designed to analyze and combine features from the pre-change and post-change images across multiple scales. The module uses a novel joint selective scan mechanism to identify significant changes, ensuring symmetry by concatenating features in multiple directions. This approach aids in effectively learning the temporal relations.
3. Mask Decoder (MD): The MD is responsible for generating the final change mask. It employs Channel-Averaged VSS (CAVSS) blocks to capture both spatial and inter-channel dependencies, a feature that sets it apart from conventional transformers or pure CNN-based methods. The decoder follows a U-Net structure with skip connections, enhancing the ability to produce accurate segmentation maps.

Experimental Results

The authors validate their approach on four well-established remote sensing datasets: WHU-CD, DSIFN-CD, LEVIR-CD, and CDD. The proposed M-CD achieves significant improvements across all evaluation metrics, including F1 score, Intersection-Over-Union (IoU), and Overall Accuracy (OA).

• WHU-CD: M-CD achieves an IoU of 91.1%, outperforming previous SOTA methods such as DDPM-CD (86.3%) and ChangeFormer (79.5%).
• DSIFN-CD: The method records an IoU of 93.5%, demonstrating robust performance over Mamba-based competitors like CDMamba (91.4%) and traditional methods like ChangeFormer (88.7%).
• LEVIR-CD: An IoU of 85.0% is reported, which is a notable improvement over previous best results from methods such as DDPM-CD (83.3%) and IFNet (78.8%).
• CDD: M-CD achieves an IoU of 96.3%, indicating its strong generalization capability and efficacy over other competitive approaches.

Implications and Future Directions

The implementation of Mamba-based architectures for CD opens several new research avenues. The linear-time scalability and enhanced receptive fields of such models showcase their potential to handle large-scale remote sensing tasks efficiently. The success of M-CD suggests that Mamba-based techniques could be employed in other computer vision tasks requiring temporal and spatial awareness, such as video segmentation or time-series forecasting.

Furthermore, the results indicate that the selective state space models can mitigate the necessity for extensive pretraining, as required by diffusion-based models. This characteristic can significantly reduce computational resources and training time, making them appealing for real-world applications.

Future research could explore several directions:

• Extend the Mamba-based approach to multi-spectral and hyper-spectral image analysis, improving the discrimination of changes over diverse wavelengths.
• Investigate the integration of Mamba-based methods with self-supervised learning techniques to further enhance their performance in scenarios with limited annotated data.
• Develop more efficient training strategies and optimizations to further reduce the computational overhead without compromising on model performance.

In conclusion, this paper presents a compelling case for utilizing Mamba-based architectures in remote sensing change detection, demonstrating substantial gains in accuracy and efficiency over contemporary methods. The innovative Siamese network design combined with multi-scale difference learning positions M-CD as a forward-thinking contribution to the remote sensing community.