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Spatial-Spectral Bidirectional-Driven Collaborative Network with Coordinate-Aware and Spectral-Modulated Interaction
Qingshan Gao1, Conghui Tao1, Xiongjun Du1
1Siwei SuperView Satellite Remote Sensing Co., Ltd., Hangzhou 310012, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces a novel bidirectional framework for hyperspectral pansharpening, enhancing spatial details while preserving spectral fidelity. The new method achieves state-of-the-art results on benchmark and real-world satellite data.
Area of Science:
- Computational imaging
- Remote sensing
- Image processing
Background:
- Hyperspectral imaging systems face hardware constraints causing coupled spatial-spectral degradations.
- Existing hyperspectral pansharpening methods struggle with spectral distortion or blurred details due to inadequate modeling of spatial-spectral coupling.
- High-resolution hyperspectral data is crucial for environmental monitoring, urban planning, and precision agriculture.
Purpose of the Study:
- To develop an advanced hyperspectral pansharpening method that overcomes limitations of existing techniques.
- To achieve simultaneous high spatial resolution and high spectral fidelity in reconstructed hyperspectral images.
- To introduce a synergistic framework for mutual guidance between spatial detail infusion and spectral fidelity preservation.
Main Methods:
- A bidirectional driving framework is proposed, integrating spatial coordinate-aware representations into a spectral self-attention module.
- Spectral importance scores modulate multi-receptive-field convolutions via channel-wise weighting for bidirectional interaction.
- A large-scale dataset from the ZY-1-02D satellite with high-fidelity PAN and HSI pairs was constructed and released.
Main Results:
- The proposed method demonstrates state-of-the-art performance in both spatial fidelity and spectral preservation.
- Extensive experiments on benchmark simulations and the ZY-1-02D dataset validate the framework's effectiveness.
- The bidirectional interaction mechanism ensures enhanced spatial reconstruction while rigorously preserving spectral integrity.
Conclusions:
- The bidirectional driving framework effectively addresses the coupled spatial-spectral degradations in hyperspectral imaging.
- The developed method offers a significant advancement for hyperspectral pansharpening applications.
- The publicly available ZY-1-02D dataset will foster future research in hyperspectral computational imaging.