用少量压缩数据实现高光谱图像无监督分类，揭示了真实场景下分类评价的局限性。
Classification non supervis{é}es d'acquisitions hyperspectrales cod{é}es : quelles v{é}rit{é}s terrain ?
- 基于类内光谱变异简化模型，从十倍压缩数据中识别类别并估计参考光谱
- 在帕维亚大学场景中成功检测出光谱更一致的区域，验证方法有效性
- 挑战传统评估标准，强调无监督分类需重新定义类别与评价方式
我们提出一种使用少量编码获取数据的无监督高光谱分类方法，针对DD-CASSI高光谱成像仪的数据。基于类内光谱变异的简单模型，该方法可在数据压缩十倍的情况下识别类别并估计参考光谱。通过帕维亚大学场景实验，我们揭示了现有地面真值评估方法的局限：类别定义模糊、类内变异大，甚至存在分类错误。研究表明，在简单假设下，仍可检测出光谱更一致的区域，强调需重新思考无监督分类方法的评估标准，尤其在真实场景中的适用性。
原文摘要 · Abstract (English)
We propose an unsupervised classification method using a limited number of coded acquisitions from a DD-CASSI hyperspectral imager. Based on a simple model of intra-class spectral variability, this approach allow to identify classes and estimate reference spectra, despite data compression by a factor of ten. Here, we highlight the limitations of the ground truths commonly used to evaluate this type of method: lack of a clear definition of the notion of class, high intra-class variability, and even classification errors. Using the Pavia University scene, we show that with simple assumptions, it is possible to detect regions that are spectrally more coherent, highlighting the need to rethink the evaluation of classification methods, particularly in unsupervised scenarios.
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