用新数学方法建模高光谱图像背景，精准识别异常像素。
Improved Hyperspectral Anomaly Detection via Unsupervised Subspace Modeling in the Signed Cumulative Distribution Transform Domain
- 将像素看作模板的变形，转换到SCDT域建模。
- 无监督建模背景信号，异常点为偏离模型的差异。
- 在5个数据集上优于现有方法，适合复杂环境应用。
高光谱异常检测（HAD）是许多民用和军事应用中的关键技术，旨在识别与多数背景光谱特征不同的异常像素。尽管已有大量研究提升HAD性能，但复杂真实环境及目标异常光谱先验知识匮乏仍带来挑战。本文提出一种新方法：通过基于传输的数学模型描述高光谱图像中的像素，将像素视为模板在未知形变下的观测，从而在符号累积分布变换（SCDT）域中表示。在此域中，采用无监督子空间建模技术构建丰富背景信号模型，并将偏离该模型的信号识别为异常。在五个不同数据集上的全面评估表明，本方法显著优于现有先进方法。
原文摘要 · Abstract (English)
Hyperspectral anomaly detection (HAD), a crucial approach for many civilian and military applications, seeks to identify pixels with spectral signatures that are anomalous relative to a preponderance of background signatures. Significant effort has been made to improve HAD techniques, but challenges arise due to complex real-world environments and, by definition, limited prior knowledge of potential signatures of interest. This paper introduces a novel HAD method by proposing a transport-based mathematical model to describe the pixels comprising a given hyperspectral image. In this approach, hyperspectral pixels are viewed as observations of a template pattern undergoing unknown deformations that enables their representation in the signed cumulative distribution transform (SCDT) domain. An unsupervised subspace modeling technique is then used to construct a model of abundant background signals in this domain, whereupon anomalous signals are detected as deviations from the learned model. Comprehensive evaluations across five distinct datasets illustrate the superiority of our approach compared to state-of-the-art methods.
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