PNEC-Mamba：用于高光谱图像分类的原型引导正负证据校准方法
PNEC-Mamba: Prototype-Guided Positive-Negative Evidence Calibration for Hyperspectral Image Classification
浏览论文内容
中文总结 AI 辅助
针对高光谱图像分类中像素表征歧义问题，提出PNEC-Mamba框架，通过正负证据校准与不确定性估计提升分类性能，在三个基准数据集上优于现有最优方法。
中文摘要 AI 辅助
在实际高光谱场景中，由于光谱相似性、混合像元及局部上下文干扰等因素，像素表征往往存在歧义，可能同时编码判别性证据与干扰信息。现有方法主要聚焦于学习更强大的表征或建模更广泛的上下文，却很少探究学习到的表征是否为分类决策提供可靠证据或引入干扰。为解决该问题，本文从像素级证据可靠性建模的视角研究高光谱图像分类，提出原型引导正负证据校准框架PNEC-Mamba。该框架逐步建立语义参考、分离类别相关证据与干扰、估计像素级可靠性并进行选择性校准：首先，全图状态空间编码器提取像素表征，动态类别原型提供与特征空间共同演化的语义参考；随后，通过像素-原型竞争生成正负证据，明确将支持分类的判别线索与竞争类别的混淆信号分离；基于这些证据关系，引入多源不确定性估计策略评估像素级可靠性，为不确定区域执行更强的证据校准；最后，应用全分辨率一致性细化步骤恢复局部空间细节，提升最终预测的边界连贯性。在三个基准数据集上的大量实验表明，PNEC-Mamba相较于现有最优方法实现了更优的分类性能。
英文摘要
In real-world hyperspectral scenes, pixel representations are often ambiguous due to factors such as spectral similarity, mixed pixels, and local context interference, which may simultaneously encode discriminative evidence and interfering information. Existing methods mainly focus on learning more powerful representations or modeling broader contexts, but rarely investigate whether the learned representations provide reliable evidence or introduce interference into classification decisions. To address this issue, we view hyperspectral image classification from the perspective of pixel-level evidence reliability modeling and propose PNEC-Mamba, a prototype-guided positive-negative evidence calibration framework. The framework progressively establishes semantic references, separates class-related evidence from interference, estimates pixel-level reliability, and performs selective calibration. First, a full-image state-space encoder extracts pixel representations, while dynamic class prototypes provide semantic references that evolve jointly with the feature space. Subsequently, positive and negative evidence is derived from pixel-prototype competition, explicitly separating discriminative cues that support classification from confusing signals associated with competing classes. Based on these evidence relationships, a multi-source uncertainty estimation strategy is introduced to assess pixel-level reliability, enabling stronger evidence calibration for uncertain regions. Finally, a full-resolution consistency refinement step is applied to recover local spatial details and improve boundary coherence in the final predictions. Extensive experiments on three benchmark datasets demonstrate that PNEC-Mamba achieves superior classification performance compared with state-of-the-art methods.
