AGSA-Net: Abundance-Guided Self-Attention Network for Spectral Unmixing-Aware Hyperspectral Remote Sensing Image Classification
TL;DR
An abundance-guided self-attention network (AGSA-Net), an abundance-guided self-attention network that explicitly integrates spectral unmixing priors into the classification process, and demonstrates the benefit of incorporating abundance-guided contextual modeling, particularly in heterogeneous urban scenes.
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 an abundance-guided self-attention network (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 nonnegativity and sum-to-one constraints, regularized by a 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
