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This paper describes a second study effort investigating the impact of hyperspectral compression on the utility of compressed and subsequently reconstructed data. The overall objective is to assess and quantify the extent to which degradation introduced by compression affects the exploitation results of the compressed-reconstructed hyperspectral data. The goal of these studies is to provide a sound empirical basis for identifying the best performing compression algorithms and establishing compression ratios acceptable for various exploitation functions. Two nonliteral exploitation functions (i.e., anomaly detection and material identification) were performed on the original and compressed-reconstructed image data produced by two new hyperspectral compression algorithms (i.e., 3D Wavelets and Karhunen-Loeve Transform [KLT] Trellis-Coded Quantizer [TCQ] based JPEG-2000) at five compression ratios (i.e., 3:1, 6:1, 12:1, 24:1, and 48:1) on two scenes (a desert background and a forest background scene). The results showed that, in general, no appreciable degradation in anomaly detection performance occurred between the compressed-reconstructed and original hyperspectral data sets for both scenes using the KLT- TCQ based JPEG-2000 algorithm over the compression ratios studied. Degradation was observed for the 3D Wavelets based JPEG-2000 algorithm at 48:1 compression ratio. As for material identification, no appreciable degradation occurred between the compressed- reconstructed and original hyperspectral data sets for the desert scene using the KLT-TCQ algorithm over all the compression ratios studied. Some degradation was observed for the forest scene at higher compression ratios. Degradation was observed for the 3D Wavelets algorithm at compression ratios of 6:1 and higher for the desert scene and at compression ratios of 24:1 and higher for the forest scene. These results were compared with those obtained in the previous study using the Unmixing/Wavelets and KLT/Wavelets compression algorithms. The results of this study, as well as our previous study, continue to point to implementing compression algorithms and compression ratios empirically determined suitable for specific exploitation functions as a viable means to significantly alleviate transmission overload.
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