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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Sparse linear filters for detection and classification in hyperspectral imagery
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Sparse linear filters for detection and classification in hyperspectral imagery

机译：用于高光谱图像检测和分类的稀疏线性滤波器

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摘要

We investigate the use of convex optimization to identify sparse linear filters in hyperspectral imagery. A linear filter is sparse if a large fraction of its coefficients are zero. A sparse linear filter can be advantageous because it only needs to access a subset of the available spectral channels, and it can be applied to high-dimensional data more cheaply than a standard linear detector. Finding good sparse filters is nontrivial because there is a combinatorially large number of discrete possibilities from which to choose the optimal subset of nonzero coefficients. But, by converting the optimality criterion into a convex loss function, and by employing an L1 penalty, one can obtain sparse solutions that are globally optimal. We investigate the performance of these sparse filters as a function of their sparsity, and compare the convex optimization approach with more traditional alternatives for feature selection. The methodology is applied both to the adaptive matched filter for weak signal detection, and to the Fisher linear discriminant for terrain categorization.
机译：我们调查使用凸优化来识别高光谱图像中的稀疏线性滤波器。如果线性滤波器的大部分系数为零，则它是稀疏的。稀疏线性滤波器可能是有利的，因为它只需要访问可用频谱通道的子集，并且与标准线性检测器相比，它可以更便宜地应用于高维数据。找到好的稀疏滤波器是不平凡的，因为有大量的离散可能性可以从中选择非零系数的最佳子集。但是，通过将最优性标准转换为凸损失函数，并通过使用L1罚分，可以获得全局最优的稀疏解。我们研究了这些稀疏滤波器的性能作为其稀疏性的函数，并将凸优化方法与更多传统的特征选择方法进行了比较。该方法既可用于弱信号检测的自适应匹配滤波器，又可用于地形分类的Fisher线性判别式。

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