13 June 2014 Denoising HSI images for standoff target detection
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Abstract
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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Steven Wilson, Steven Wilson, Shahram Latifi, Shahram Latifi, } "Denoising HSI images for standoff target detection", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908812 (13 June 2014); doi: 10.1117/12.2050165; https://doi.org/10.1117/12.2050165
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