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Denoising HSI images for standoff target detection

机译：对HSI图像进行降噪以检测对峙目标

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Hyperspectral image denoising methods aim to improve the spatial and spectral quality of the image to increase the effectiveness of target detection algorithms. Comparing denoising methods is difficult, because sometimes authors have compared their algorithms to simple methods such as Wiener filter and wavelet thresholding. We would like to compare only the most effective methods for standoff target detection using sampled training spectra. Our overall goal is to implement an HSI algorithm to detect possible weapons and shielding materials in a scene, using a lab collected library of materials spectra. Selection of a suitable method is based on PSNR, classification accuracy, and time complexity. Since our goal is target detection, classification accuracy is more emphasized; however, an algorithm that requires large processing time would not be effective for the purpose of real-time detection. Elapsed time between HSI data collection and its processing could allow changes or movement in the scene, decreasing the validity of results. Based on our study, the First Order Roughness Penalty algorithm provides computation time of less than 2 seconds, but only provides an overall accuracy of 88% for the Indian Pines dataset. The Spectral Spatial Adaptive Total Variation method increases overall accuracy to almost 97%, but requires a computation time of over 50 seconds. For standoff target detection, Spectral Spatial Adaptive Total Variation is preferable, because it increases the probability of classification. By increasing the percentage of weapons materials that are correctly identified, further actions such as inspection or interception can be determined with confidence.
机译：高光谱图像降噪方法旨在改善图像的空间和光谱质量，以提高目标检测算法的效率。比较去噪方法很困难，因为有时作者将它们的算法与简单的方法（如维纳滤波器和小波阈值）进行了比较。我们只想比较使用采样训练频谱进行对峙目标检测的最有效方法。我们的总体目标是使用实验室收集的材料光谱库，实现一种HSI算法，以检测场景中可能存在的武器和屏蔽材料。根据PSNR，分类精度和时间复杂度选择合适的方法。由于我们的目标是目标检测，因此更加强调分类准确性；但是，需要大量处理时间的算法对于实时检测的目的将无效。 HSI数据收集与其处理之间经过的时间可能会导致场景发生变化或移动，从而降低了结果的有效性。根据我们的研究，“一阶粗糙度惩罚”算法提供的计算时间少于2秒，但对于Indian Pines数据集而言，其总体准确度仅为88％。频谱空间自适应总变化方法将整体精度提高到将近97％，但是需要50秒钟以上的计算时间。对于隔离目标检测，“频谱空间自适应总变化”是可取的，因为它会增加分类的可能性。通过增加正确识别的武器材料的百分比，可以放心地确定进一步的行动，例如检查或拦截。

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