New PDF release: Hyperspectral Data Compression

Nonfiction 8

By Mark R. Pickering, Michael J. Ryan (auth.), Giovanni Motta, Francesco Rizzo, James A. Storer (eds.)

ISBN-10: 0387285792

ISBN-13: 9780387285795

ISBN-10: 0387286004

ISBN-13: 9780387286006

HYPERSPECTRAL info COMPRESSION provides the latest ends up in the sphere of compression of distant sensing 3D information, with a spotlight on multispectral and hyperspectral imagery. This publication is key for researchers operating throughout similar fields together with: multi-dimensional info compression, multispectral and hyperspectral information records, distant sensing, medical snapshot processing, army and aerospace photo processing, snapshot segmentation, snapshot type, and objective detection.

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Extra resources for Hyperspectral Data Compression

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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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Hyperspectral Data Compression by Mark R. Pickering, Michael J. Ryan (auth.), Giovanni Motta, Francesco Rizzo, James A. Storer (eds.)

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