发表机构
Aerospace Information Research Institute, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Guangzhou University（中国科学院空天信息创新研究院; 中国科学院大学; 广州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对高光谱图像分类中像素关系建模问题，提出DAPGNet。该网络将物理先验融入图学习，经多步骤构建拓扑、进行图扩散等操作，通过跨尺度融合优化。实验表明其在多个数据集上性能最优，提升了分类准确率，验证了各模块的互补作用。
AI 中文摘要
高光谱图像（HSI）分类需要在光谱变异性、混合像素和异质边界下进行可靠的像素关系建模。现有基于图的HSI分类器通常从空间邻近性、超像素连通性或学习到的特征亲和力构建图拓扑。然而，相邻波段携带的光谱物理先验对拓扑估计和消息传播的影响有限。本文提出了DAPGNet，一种动态自适应物理引导图扩散网络，将结构约束的物理先验注入关系级图学习中。DAPGNet首先将相邻光谱响应编码为节点级多尺度物理先验表示。然后，一个两阶段图构造器结合光谱-空间亲和力、物理先验一致性和空间距离，形成一个物理先验感知的稀疏拓扑。在图扩散过程中，学习到的边权重被转换为加法注意力偏差，而一个物理门在图聚合特征和投影物理先验特征之间进行节点级和特征级插值。跨尺度融合集成来自不同扩散深度的节点状态，并且网络通过主要分类、辅助监督和二阶光谱平滑正则化进行优化。在印度松、WHU-Hi-LongKou, Houston2013和Houston2018上的实验表明，DAPGNet在代表性的基于CNN、Transformer、Mamba和图的基线中实现了最佳的OA、AA和Kappa。在四个数据集上，它比最强的竞争方法将AA提高了3.64到7.3个百分点。消融和敏感性分析进一步支持了物理先验提取、先验感知拓扑构造、物理门控传播和光谱平滑正则化的互补作用。
英文摘要
Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topology from spatial proximity, superpixel connectivity, or learned feature affinity. However, the spectral physical prior carried by contiguous bands has limited influence on topology estimation and message propagation. This paper presents DAPGNet, a dynamic adaptive physics-guided graph diffusion network that injects a structure-constrained physical prior into relation-level graph learning. DAPGNet first encodes contiguous spectral responses into node-wise multiscale physical-prior representations. A two-stage graph constructor then combines spectral-spatial affinity, physical-prior consistency, and spatial distance to form a physical-prior-aware sparse topology. During graph diffusion, learned edge weights are transformed into additive attention biases, while a physical gate performs node-wise and feature-wise interpolation between graph-aggregated features and projected physical-prior features. Cross-scale fusion integrates node states from different diffusion depths, and the network is optimized with main classification, auxiliary supervision, and second-order spectral smoothness regularization. Experiments on Indian Pines, WHU-Hi-LongKou, Houston2013, and Houston2018 show that DAPGNet achieves the best OA, AA, and Kappa among representative CNN-, Transformer-, Mamba-, and graph-based baselines. It improves AA over the strongest competing method by 3.64 to 7.31 percentage points across the four datasets. Ablation and sensitivity analyses further support the complementary effects of physical-prior extraction, prior-aware topology construction, physics-gated propagation, and spectral smoothness regularization.
Comments9 figures and 9 tables. Pengkun Wang and Weijia Cao contributed equally to this work
