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Spectral-Spatial Dynamic Scan Mamba for Multi-Source Remote Sensing Data Classification.

Puhong Duan, Yaqi Shang, Zhiyu Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 11, 2026

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This study introduces a novel Spectral-Spatial Dynamic Scan Mamba (SDSM) for advanced multi-source remote sensing data classification. The SDSM method improves spectral-spatial feature extraction and cross-modal fusion for enhanced ground object categorization.

Area of Science:

Remote Sensing and Geospatial Analysis
Artificial Intelligence and Machine Learning
Data Fusion and Classification

Background:

Multi-source remote sensing data classification integrates diverse data (hyperspectral image, LiDAR, SAR) to categorize ground objects.
Existing Mamba-based methods use fixed scanning patterns, limiting spectral-spatial information characterization.
Current fusion techniques often overlook complementary inter-modal characteristics, using simple concatenation or attention.

Purpose of the Study:

To propose a Spectral-Spatial Dynamic Scan Mamba (SDSM) for improved multi-source remote sensing data classification.
To address limitations in spectral-spatial feature extraction and heterogeneous feature fusion.
To enhance the characterization of complementary information across different remote sensing data modalities.

Related Experiment Videos

Main Methods:

Developed a dynamic scan Mamba network for adaptive spectral-spatial feature extraction from multi-source data.
Introduced a dynamic scan module to dynamically capture salient spatial and spectral information.
Proposed a bidirectional cross-modal fusion rule incorporating a global-local frequency feature extraction module for guided heterogeneous feature fusion.

Main Results:

The proposed SDSM method demonstrated superior performance on four benchmark multi-source remote sensing datasets (MUUFL, Augsburg, Italy, Yellow River).
Achieved state-of-the-art quantitative and qualitative results compared to existing classification methods.
The dynamic scan module effectively captured crucial spectral-spatial details, and the fusion rule successfully merged heterogeneous features.

Conclusions:

The SDSM method offers a significant advancement in multi-source remote sensing data classification by enhancing feature extraction and fusion.
The adaptive nature of the dynamic scan module and the guided fusion strategy are key to the method's success.
The findings suggest a promising direction for leveraging diverse remote sensing data through sophisticated deep learning architectures.