Problem
Research questions and friction points this paper is trying to address.
Hyperspectral Image Classification
Spectral Redundancy
Noise Sensitivity
Spectral Dependencies
AGSA-Net: Abundance-Guided Self-Attention Network for Spectral Unmixing-Aware Hyperspectral Remote Sensing Image Classification
🤖 AI Summary
为解决高光谱图像分类中的光谱冗余、噪声敏感及建模难题，提出AGSA-Net，通过引入丰度引导的自注意力机制来改善分类性能。
📝 Abstract
Hyperspectral image (HSI) classification plays a vital role in remote sensing applications, including agriculture, environmental monitoring, and urban analysis. However, its performance remains challenged by high spectral redundancy, noise sensitivity, and the difficulty of jointly modeling local material composition and long-range spectral dependencies. To address this, we propose AGSA-Net, an abundance-guided self-attention network that explicitly integrates spectral unmixing priors into the classification process. AGSA Net first estimates physically meaningful subpixel abundance maps subject to non-negativity and sum-to-one constraints, regularized by hybrid linear-nonlinear reconstruction decoder. The learned abundances are then used to construct an abundance affinity prior that guides a spectral transformer to emphasize class-discriminative interactions, and the resulting transformer features are fused with compact abundance descriptors for final prediction; in contrast to existing approaches that use abundance as auxiliary or concatenated features. Experiments on Indian Pines, Augsburg, and Berlin demonstrate the benefit of incorporating abundance- guided contextual modeling, particularly in heterogeneous urban scenes. The source code and trained models are available at: https://github.com/nnuvi/AGSA-Net
Innovation
Methods, ideas, or system contributions that make the work stand out.
abundance-guided self-attention
spectral unmixing priors
hybrid linear-nonlinear reconstruction
abundance affinity prior
spectral transformer
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