Foy Bernard R, Theiler James, Fraser Andrew M
Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Opt Express. 2009 Sep 28;17(20):17391-411. doi: 10.1364/OE.17.017391.
We present an approach to the problems of weak plume detection and sub-pixel target detection in hyperspectral imagery that operates in a two-dimensional space. In this space, one axis is a matched-filter projection of the data and the other axis is the magnitude of the residual after matched-filter subtraction. Although it is only two-dimensional, this space is rich enough to include several well-known signal detection algorithms, including the adaptive matched filter, the adaptive coherence estimator, and the finite-target matched filter. Because this space is only two-dimensional, adaptive machine learning methods can produce new plume detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this matched-filter-residual space, and compare the performance of the resulting nonlinearly adaptive detector to well-known alternatives.
我们提出了一种解决高光谱图像中弱羽状物检测和亚像素目标检测问题的方法，该方法在二维空间中运行。在这个空间中，一个轴是数据的匹配滤波器投影，另一个轴是匹配滤波器减法后残差的幅度。尽管它只是二维的，但这个空间足够丰富，包含了几种著名的信号检测算法，包括自适应匹配滤波器、自适应相干估计器和有限目标匹配滤波器。由于这个空间只是二维的，自适应机器学习方法可以产生新的羽状物探测器，而不会受到维数灾难的阻碍。我们特别研究了支持向量机在这个匹配滤波器-残差空间中学习边界的效用，并将所得非线性自适应探测器的性能与著名的替代方法进行比较。
