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Plaza et. al. [67] used a new extended morphological filtering approach to find end-members based on spatially coherent regions of the image. Their results showed significantly improved classification accuracy when compared with other techniques for determining end-members. Du and Chang [68] developed a complete compression system based on linear mixture analysis. The system consists of an unsupervised method for determining end-members and abundance fractions. The abundance fractions for each pixel are transmitted and used to reconstructed version of the original data.

Brislawn C. , "Feature Extraction From Hyperspectral Images Compressed Using The JPEG-2000 Standard", Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 168-172, 7-9 Apr. 2002. Sunghyun Lim, Kwang Hoon Sohn and Chulhee Lee, "Principal Component Analysis for Compression of Hyperspectral Images", IEEE Geoscience and Remote Sensing Symposium IGARSS '01, vol. 1, 9-13 Jul. 34 [61] [62] [63] [64] [65] [66] [67] [68] [69] HYPERSPECTRAL DATA COMPRESSION 2001. Sunghyun Lim, Kwanghoon Sohn and Chulhee Lee, "Compression For Hyperspectral Images Using Three Dimensional Wavelet Transform", IEEE Geoscience and Remote Sensing Symposium IGARSS '01, vol.

Therefore, context size is fixed to 4. 3 Algorithm Parameters The definition for a context match is a critical part of the algorithm. There are two methods available. Given a sequence of pixels Y = yi^yi^ • • • ? P)t, we declared the pixel yi^m to be a member of Cic{a) if: • |oc/-p/| < r i , / = l , 2 , - - - , , ^ , o r • p < r2, where p is the correlation coefficient between Y and p. Note that neither of these matches partition the space of conditioning contexts into disjoint sets as would be the case if we used a vector quantizer to reduce the number of contest.

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