Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis
(Sprache: Englisch)
We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of...
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Klappentext zu „Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis “
We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands.
Inhaltsverzeichnis zu „Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis “
Innovative Imaging, Parsing Patterns and Motivating Models.- Pattern Progressions and Segmentation Sequences for IMAGE Intensity Modeling and Grouped Enhancement.- Collective and Composite Contrast for Pattern Pictures.- Content Classification and Thematic Transforms.- Comparative Change and Pattern Perturbation.- Conjunctive Context.- Advanced Aspects and Anticipated Applications.
Autoren-Porträt von Wayne L. Myers, Ganapati P. Patil
Dr. Wayne L. Myers earned M.F. and Ph.D. degrees in forest ecology and forest entomology at the University of Michigan. He began his professional career in Canada as a research forest entomologist and biometrician. He then joined the faculty of forestry at Michigan State University specializing in biometrics and remote sensing. The position at Michigan State also encompassed consultancies with the U.S. Forest Service and a work in Brazil. He moved to Penn State University in 1978 in the School of Forest Resources. He is professor of forest biometrics and Director of the Office for Remote Sensing and Spatial Information Resources (ORSSIR) in the Penn State Institutes of Environment. He has thirty-five years of experience in research on development of remote sensing, geographic information systems, and related spatial technologies with applications focusing on natural resources and environment. This extends back to participation as a co-investigator in early investigations of ERTS/LANDSAT as the first spaceborne civilian multispectral sensor.
His recent research has focused on dual level progressive segmentation of multispectral images for purposes of compression, integration with geographic information systems and pattern-based change detection. He has developed concepts and computation of echelons of spatial structure in digital surfaces that facilitate extracting major change features from change indicator images. Echelons offer alternatives to thresholding in surface or pseudo-surface rasters. Dome domains provide a further generalization of topological structure in signal surfaces.
He has extensive international experience including long-term advisory for the U.S. Agency for International Development in India and research fellowships in Malaysia. He has placed special emphasis on interdisciplinary research and team approach.
Bibliographische Angaben
- Autoren: Wayne L. Myers , Ganapati P. Patil
- 2010, XVIII, 190 Seiten, Masse: 15,6 x 23,4 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1441942718
- ISBN-13: 9781441942715
Sprache:
Englisch
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