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Comparison of adaptive array-processing schemes for land mine detection using hyperspectral imagery

机译:利用高光谱图像对土地检测自适应阵列处理方案的比较

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Adaptive techniques for detecting small or difficult targets in the midst of high noise and/or clutter have a rich history in the radar array processing community. However, the utility of these schemes is only beginning to be realized for multichannel electro-optical techniques, specifically hyperspectral imaging (HSI). The data products generated by hyperspectral sensors differ greatly from those of radar and sonar arrays, yet recent studies using HSI data have offered promising results for modified versions of common adaptive detectors. In this paper, we compare a series popular adaptive detection schemes applied to HSI data for the task of land mine detection. Experiments using real hyperspectral image cubes, not simulations, are performed with data from both the visible-SWIR and LWIR regions. Results are presented for different mine types in a variety of scenes.
机译:在高噪声和/或杂波中检测小或困难目标的自适应技术在雷达阵列处理社区中具有丰富的历史。然而,这些方案的效用仅开始实现多通道电光技术,特别是高光谱成像(HSI)。由高光谱传感器产生的数据产品从雷达和声纳阵列的差异很大,但最近使用HSI数据的研究已经为普通自适应检测器的修改版本提供了有希望的结果。在本文中,我们比较适用于HSI数据的系列流行自适应检测方案,以进行陆地矿地检测任务。使用真实高光谱图像立方体的实验,而不是模拟,来自来自可见SWIR和LWIR区域的数据。结果在各种场景中呈现不同的矿山类型。

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