Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914066595840000 |
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| author | Jiang, Yizhen Ma, Mengting Zhu, Anqi Ma, Xiaowen Li, Jiaxin Zhang, Wei |
| author_facet | Jiang, Yizhen Ma, Mengting Zhu, Anqi Ma, Xiaowen Li, Jiaxin Zhang, Wei |
| contents | Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images, finally generating the high-resolution multi-spectral (HR-MS) images. Although deep learning-based models have achieved excellent performance, they often come with high computational complexity, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening. Nevertheless, there are two main issues with binarizing pan-sharpening models: (i) the binarization will cause serious spectral distortion due to the inconsistent spectral distribution of the PAN/LR-MS images; (ii) the common binary convolution kernel is difficult to adapt to the multi-scale and anisotropic spatial features of remote sensing objects, resulting in serious degradation of contours. To address the above issues, we design the customized spatial-spectral binarized convolution (S2B-Conv), which is composed of the Spectral-Redistribution Mechanism (SRM) and Gabor Spatial Feature Amplifier (GSFA). Specifically, SRM employs an affine transformation, generating its scaling and bias parameters through a dynamic learning process. GSFA, which randomly selects different frequencies and angles within a preset range, enables to better handle multi-scale and-directional spatial features. A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized pan-sharpening method can attain a promising performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_23321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion Jiang, Yizhen Ma, Mengting Zhu, Anqi Ma, Xiaowen Li, Jiaxin Zhang, Wei Computer Vision and Pattern Recognition Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images, finally generating the high-resolution multi-spectral (HR-MS) images. Although deep learning-based models have achieved excellent performance, they often come with high computational complexity, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening. Nevertheless, there are two main issues with binarizing pan-sharpening models: (i) the binarization will cause serious spectral distortion due to the inconsistent spectral distribution of the PAN/LR-MS images; (ii) the common binary convolution kernel is difficult to adapt to the multi-scale and anisotropic spatial features of remote sensing objects, resulting in serious degradation of contours. To address the above issues, we design the customized spatial-spectral binarized convolution (S2B-Conv), which is composed of the Spectral-Redistribution Mechanism (SRM) and Gabor Spatial Feature Amplifier (GSFA). Specifically, SRM employs an affine transformation, generating its scaling and bias parameters through a dynamic learning process. GSFA, which randomly selects different frequencies and angles within a preset range, enables to better handle multi-scale and-directional spatial features. A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized pan-sharpening method can attain a promising performance. |
| title | Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23321 |