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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Simultaneous Joint Sparsity Model for Target Detection in Hyperspectral Imagery
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Simultaneous Joint Sparsity Model for Target Detection in Hyperspectral Imagery

机译：高光谱影像中目标检测的同时联合稀疏模型

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摘要

This letter proposes a simultaneous joint sparsity model for target detection in hyperspectral imagery (HSI). The key innovative idea here is that hyperspectral pixels within a small neighborhood in the test image can be simultaneously represented by a linear combination of a few common training samples but weighted with a different set of coefficients for each pixel. The joint sparsity model automatically incorporates the interpixel correlation within the HSI by assuming that neighboring pixels usually consist of similar materials. The sparse representations of the neighboring pixels are obtained by simultaneously decomposing the pixels over a given dictionary consisting of training samples of both the target and background classes. The recovered sparse coefficient vectors are then directly used for determining the label of the test pixels. Simulation results show that the proposed algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors, as well as binary classifiers such as support vector machines.
机译：这封信提出了一种同时联合的稀疏模型，用于高光谱图像（HSI）中的目标检测。此处的关键创新思想是，可以通过几个常用训练样本的线性组合同时表示测试图像中一个小邻域内的高光谱像素，但是每个像素使用一组不同的系数加权。通过假设相邻像素通常由相似的材料组成，联合稀疏模型​​自动将像素间相关性合并到HSI中。通过同时分解由目标和背景类别的训练样本组成的给定字典上的像素，可以获得相邻像素的稀疏表示。然后将恢复的稀疏系数矢量直接用于确定测试像素的标记。仿真结果表明，该算法优于经典的高光谱目标检测算法，如流行的光谱匹配滤波器，匹配子空间检测器，自适应子空间检测器以及支持向量机等二进制分类器。

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