Download PDF by Wayne L. Myers: Pattern-Based Compression of Multi-Band Image Data for

By Wayne L. Myers

ISBN-10: 0387444343

ISBN-13: 9780387444345

ISBN-10: 0387444394

ISBN-13: 9780387444390

This publication describes an built-in method of utilizing remotely sensed info at the side of geographic info structures for panorama research. Remotely sensed facts are compressed into an analytical image-map that's suitable with the preferred geographic info structures in addition to freeware audience. The procedure is premiere for landscapes that show a suggested mosaic development of land disguise.

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Extra info for Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis (Environmental and Ecological Statistics)

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Scepan and J. M. Scott. 1990. An Information Systems Approach to Biological Diversity. International Journal of Geographic Information Systems 4: 55-78. DeMers, M. 2000. Fundamentals of GIS, 2"" ed. New York: John Wiley & Sons. 498 p. Frohn, R. C. 1998. Remote Sensing for Landscape Ecology: New Metric Indicators for Monitoring, Modeling, and Assessment of Ecosystems. Boca Raton, FL: Lewis Publishers. 99 p. Forman, R. T. T. 1995. Land Mosaics: the Ecology of Landscapes and Regions. : Cambridge Univ.

9) where MpO = partial prominence as fraction of pixels in current partial image; k = kth pattern; HI, = nearest neighbor of k in signal space by using distance function D,,; D„ = (optionally weighted) Euclidean distance. Then a first-stage process for potential patterns (conditional on order of pixels in an image) operates as follows. Begin by obtaining the first 250 non-duplicate pixels from the file, using any duplication as frequency counts for mass functions. If there are no duplicates in this initial part of the image file, then the starting potentials are the same as for the ascenario.

To avoid possible confusion with statistical clusters, we refer to spatially connective aggregations of pattern elements as patches, instances or connected components. As an innovative part of our extended treatment of patterns, we introduce here a way to have strong pattern 'overtones' in an image model while reducing the attendant loss of 'undertone' detail. This is by multilevel indexing of patterns to form nested patterns. Thus, we can have a first (strong) level of numbered patterns along with a table of proxy signal vectors expressing central tendencies for those strong patterns.

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Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis (Environmental and Ecological Statistics) by Wayne L. Myers


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