发表机构
North South University（南北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高光谱图像分类中的冗余和噪声问题，提出丰度引导的自注意力网络AGSA-Net，融合光谱解混先验与变换器，在多个数据集上提升分类性能。
AI 中文摘要
高光谱图像（HSI）分类在遥感应用中扮演着至关重要的角色，包括农业、环境监测和城市分析。然而，其性能仍受到高光谱冗余、噪声敏感性以及难以联合建模局部物质组成和长距离光谱依赖性的挑战。为了解决这一问题，我们提出了AGSA-Net，一种丰度引导的自注意力网络，将光谱解混先验显式地整合到分类过程中。AGSA-Net首先在非负性和和为一约束下估计物理上有意义的亚像素丰度图，并通过混合线性-非线性重建解码器进行正则化。然后，利用学习到的丰度构建丰度亲和先验，引导光谱变换器强调类别判别性交互，并将得到的变换器特征与紧凑的丰度描述符融合以进行最终预测；这与现有方法将丰度用作辅助或拼接特征的做法形成对比。在Indian Pines、Augsburg和Berlin上的实验证明了引入丰度引导的上下文建模的益处，特别是在异质城市场景中。源代码和训练模型可在以下网址获取：this https URL
英文摘要
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
CommentsThis paper has been accepted at IEEE Transactions on Geoscience and Remote Sensing (TGRS)
