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Hyperspectral target detection via locality-constrained group sparse representation

机译：通过局域约束的群体稀疏表示进行高光谱目标检测

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摘要

Target detection is an important task in hyper-spectral image processing. Traditional methods usually impose a stringent assumption on the spectrum distribution of the background and targets, which cannot hold for all the practical situations. This problem can be avoided by sparsity-based method in which each test pixel is represented by a linear combination of a few samples from an overcomplete dictionary. However, classical sparsity model ignores the dictionary structure and cannot guarantee an accurate sparse representation for the test pixel. Motivated by this point, this paper proposes a locality-constrained group sparse representation for target detection. It makes full use of the dictionary structure and preserves the manifold of the original data at the same time, not only ensuring that the correlated training samples belonging to the correct class are used to express the test pixel but also guaranteeing that similar spectrums of HSI pixels will have similar codes. Experimental results on real hyperspectral imagery suggest that the proposed method is more effective than conventional sparsity-based algorithm and the statistics-based methods.
机译：目标检测是高光谱图像处理中的重要任务。传统方法通常会对背景和目标的光谱分布施加严格的假设，而这并不适合所有实际情况。可以通过基于稀疏性的方法避免此问题，在该方法中，每个测试像素都由来自不完整字典的几个样本的线性组合表示。但是，经典稀疏模型忽略了字典结构，无法保证测试像素的准确稀疏表示。基于这一点，本文提出了一种用于目标检测的局域约束群稀疏表示。它充分利用了字典结构，并同时保留了原始数据的多种形式，不仅确保了使用属于正确类别的相关训练样本来表达测试像素，而且还保证了类似的HSI像素频谱将具有类似的代码。在真实的高光谱图像上的实验结果表明，该方法比传统的基于稀疏性的算法和基于统计的方法更为有效。

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