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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Nonlocal Similarity Regularized Sparsity Model for Hyperspectral Target Detection
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Nonlocal Similarity Regularized Sparsity Model for Hyperspectral Target Detection

机译：高光谱目标检测的非局部相似度正则稀疏模型

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摘要

Sparsity-based approaches have been considered useful for target detection in hyperspectral imagery. Based on the sparse reconstruction theory, the vectors representing the spectral signature of hyperspectral pixels can be a linear combination of linearly dependent training vectors. The training vectors constitute an overcomplete dictionary, which allow for sparse representations for test pixel vectors as only a few of training vectors are used. Such sparsity can be applied in hyperspectral target detection. However, since the sparse decomposition has the potential instability, similar data often have different estimates. In this letter, we propose a nonlocal similarity regularized sparsity model to deal with the problem. Nonlocal similarity enhances classical sparsity model as it preserves the manifold structure of original data and makes more stable estimations for similar data. In addition, the nonlocal sparsity model is effectively solved with a developed greedy algorithm. Experimental results suggest an advantage of the nonlocal sparsity model over conventional sparsity models and a better performance of the proposed algorithm compared with conventional sparsity-based algorithms.
机译：基于稀疏性的方法已被认为可用于高光谱图像中的目标检测。基于稀疏重建理论，代表高光谱像素的光谱特征的向量可以是线性相关训练向量的线性组合。训练向量构成了一个不完整的字典，由于仅使用了少数训练向量，因此它允许测试像素向量的稀疏表示。这种稀疏性可以应用于高光谱目标检测。但是，由于稀疏分解具有潜在的不稳定性，因此相似的数据通常具有不同的估计。在这封信中，我们提出了一个非局部相似正则化稀疏模型来处理该问题。非局部相似性增强了经典稀疏性模型，因为它保留了原始数据的流形结构并为相似数据做出了更稳定的估计。此外，使用开发的贪婪算法可以有效地解决非局部稀疏模型。实验结果表明，与传统的基于稀疏性的算法相比，非局部稀疏性模型具有优势，并且所提出的算法具有更好的性能。

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