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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images
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A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images

机译：基于稀疏表示的二元假设模型用于高光谱图像目标检测

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摘要

In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance.
机译：本文提出了一种新的基于稀疏表示的二元假设（SRBBH）模型用于高光谱目标检测。所提出的方法依赖于稀疏表示引起的未知样本的二元假设模型。样本可以由零假设下的仅背景字典的训练样本和替代假设下的目标和背景字典的训练样本稀疏表示。可以通过贪婪算法来恢复模型中的稀疏向量，并且两个假设都采用相同的稀疏性级别。因此，恢复过程导致纯背景子空间与目标和背景子空间之间的竞争，这些竞争直接由不同的假设表示。可以通过比较不同假设下的重建残差来做出检测决策。在高光谱图像上进行了广泛的实验，表明SRBBH模型具有出色的检测性能。

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