Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion

• URL: http://arxiv.org/abs/2509.23321v2
• Date: Tue, 30 Sep 2025 15:04:23 GMT
• Title: Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion
• Authors: Yizhen Jiang, Mengting Ma, Anqi Zhu, Xiaowen Ma, Jiaxin Li, Wei Zhang,
• Abstract summary: Deep learning models have achieved excellent performance, but they often come with high computational complexity.<n>In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening.<n>A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet.
• Score: 25.15016853820625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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