发表机构
North Carolina State University; Wilson College of Textiles（北卡罗来纳州立大学; 威尔逊纺织学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出无泄漏评估协议，发现十种高光谱分类架构在空间不相交评估下均存在共享的光谱模糊性问题，导致Macro-F1下降且排名变化。
AI 中文摘要
高光谱图像分类仍严重依赖单一场景内的随机像素划分。Salinas数据集经随机划分后，是用于比较不同架构的最广泛使用的数据集之一。然而，在随机划分方法下，大量测试像素紧邻训练像素，这会夸大报告的准确率。本研究引入了一种无泄漏评估协议，将空间分离与模型的感受野关联起来。将该协议应用于十种不同架构，包括经典架构、光谱架构、光谱-空间架构、Transformer架构、视觉骨干架构及状态空间架构，结果显示Macro-F1平均下降0.147，模型排名变化最多达五位。此外，无泄漏评估限制了在给定基准上可测试的架构，由于每个划分仅支持有限半径内的图像块，报告该半径与感受野对于公平比较至关重要。另外，本研究揭示所有十种架构在很大程度上对相同像素进行了错误分类，表明数据中存在一种光谱模糊性，而没有任何一种架构能够解决这一问题。
英文摘要
Hyperspectral image classification still relies heavily on random pixel splits within a single scene. The Salinas dataset, randomly split, is among the most widely used datasets for comparing different architectures. However, under a random split method, a large fraction of test pixels fall immediately adjacent to a training pixel, which inflates reported accuracy. This work introduces a leakage-free evaluation protocol linking spatial separation to the model's receptive field. Applying this protocol across ten different architectures, including classical, spectral, spectral-spatial, transformer, vision-backbone, and state-space families, shows that Macro-F1 drops by 0.147 on average and model rankings change by as many as five places. Furthermore, leakage-free evaluation limits which architectures can be tested on a given benchmark. Since each partition supports patches only within a finite radius, reporting this radius alongside the receptive field is essential for fair comparison. In addition, this study reveals that all ten architectures misclassify largely the same pixels, pointing to a spectral ambiguity in the data that none of them resolves.
