Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model's true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks. By separating training, validation, and test data without overlap, the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or validation. Experiments demonstrate the approach significantly improves a model's generalization compared to alternatives that include training and validation data in test data. By eliminating data leakage between sets, disjoint sampling provides reliable metrics for benchmarking progress in HSIC. Researchers can have confidence that reported performance truly reflects a model's capabilities for classifying new scenes, not just memorized pixels. This rigorous methodology is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral sensors. The source code is available at https://github.com/mahmad00/Disjoint-Sampling-for-Hyperspectral-Image-Classification.

翻译：不相交采样对于严格且无偏地评估最先进（SOTA）模型至关重要。当训练集、验证集和测试集存在重叠或共享数据时，会引入偏差，导致性能指标虚高，从而阻碍对模型泛化新样本真实能力的准确评估。本文提出了一种创新的不相交采样方法，用于在高光谱图像分类（HSIC）任务中训练SOTA模型。通过分离训练、验证和测试数据，确保无重叠，所提方法有助于更公平地评估模型对训练或验证过程中未见过像素的分类能力。实验表明，与将训练和验证数据纳入测试数据的替代方案相比，该方法显著提升了模型的泛化能力。通过消除数据集之间的数据泄露，不相交采样为衡量HSIC领域进展提供了可靠的指标。研究人员可以确信，所报告的性能真实反映了模型对分类新场景（而非仅记忆像素）的能力。这种严格的方法论对于推动SOTA模型的发展及其在高光谱传感器大规模土地测绘中的实际应用至关重要。源代码见https://github.com/mahmad00/Disjoint-Sampling-for-Hyperspectral-Image-Classification。