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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Adaptive Smoothing for Subpixel Target Detection in Hyperspectral Imaging
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Adaptive Smoothing for Subpixel Target Detection in Hyperspectral Imaging

机译：高光谱成像中亚像素目标检测的自适应平滑

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摘要

In practical target detection, we often deal with situations where even a relatively small target is present in two or more adjacent pixels, due to its physical configuration with respect to the pixel grid. At the same time, a relatively large but narrow object (such as a wall or a narrow road) may be collectively present in many pixels but be only a small part of each single pixel. In such cases, critical information about the target is spread among many spectra and cannot be used efficiently by detectors that investigate each single pixel separately. We show that these difficulties can be overcome by using appropriate smoothing operators. We introduce a class of Locally Adaptive Smoothing detectors and evaluate them on three different images representing a broad range of blur that would interfere with the detection process in practical problems. The smoothing-based detectors prove to be very powerful in these cases, and they outperform the traditional detectors such as the constrained energy minimization (CEM) filter or the one-dimensional target-constrained interference-minimized filter (TCIMF).
机译：在实际目标检测中，由于其相对于像素网格的物理配置，我们经常处理这样的情况，即使在两个或多个相邻像素中甚至存在相对较小的目标。同时，相对较大但狭窄的对象（例如墙壁或狭窄的道路）可能会共同出现在许多像素中，但仅占每个像素的一小部分。在这种情况下，有关目标的关键信息会散布在许多光谱中，并且不能由分别研究每个单个像素的探测器有效地使用。我们表明，通过使用适当的平滑运算符可以克服这些困难。我们介绍了一类局部自适应平滑检测器，并在代表不同模糊范围的三幅不同图像上对它们进行评估，这些模糊域会干扰实际问题中的检测过程。在这种情况下，基于平滑的检测器非常强大，并且其性能优于传统的检测器，例如约束能量最小化（CEM）滤波器或一维目标约束干扰最小化滤波器（TCIMF）。

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