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.
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