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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Detection of Subpixel Targets on Hyperspectral Remote Sensing Imagery Based on Background Endmember Extraction
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Detection of Subpixel Targets on Hyperspectral Remote Sensing Imagery Based on Background Endmember Extraction

机译：基于背景末端提取的高光谱遥感图像对亚像素目标的检测

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摘要

The low spatial resolution associated with imaging spectrometers has caused subpixel target detection to become a special problem in hyperspectral image (HSI) processing that poses considerable challenges. In subpixel target detection, the size of the target is smaller than that of a pixel, making the spatial information of the target almost useless so that a detection algorithm must rely on the spectral information of the image. To address this problem, this article proposes a subpixel target detection algorithm for hyperspectral remote sensing imagery based on background endmember extraction. First, we propose a background endmember extraction algorithm based on robust nonnegative dictionary learning to obtain the background endmember spectrum of the image. Next, we construct a hyperspectral subpixel target detector based on pixel reconstruction (HSPRD) to perform pixel-by-pixel target detection on the image to be tested using the background endmember spectral matrix and the spectra of known ground targets. Finally, the subpixel target detection results are obtained. The experimental results show that, compared with other existing subpixel target detection methods, the algorithm proposed here can provide the optimum target detection results for both synthetic and real-world data sets.
机译：与成像光谱仪相关联的低空间分辨率使子像素目标检测成为高光谱图像（HSI）处理中的特殊问题，其造成显着挑战。在子像素目标检测中，目标的大小小于像素的大小，使目标的空间信息几乎无用，使得检测算法必须依赖于图像的光谱信息。为了解决这个问题，本文提出了一种基于背景末端提取的超光谱遥感图像的子像素目标检测算法。首先，我们提出了一种基于鲁棒非环境典型词典学习的背景终端补充算法，以获得图像的背景终点谱。接下来，我们基于像素重建（HSPRD）构建高光谱子像素目标检测器，以使用背景端部谱矩阵和已知地面目标的频谱对待测图像上的像素 - 逐像素目标检测。最后，获得子像素靶检测结果。实验结果表明，与其他现有的子像素目标检测方法相比，这里提出的算法可以为合成和实世界数据集提供最佳目标检测结果。

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