Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis / Edition 1

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Overview

This book describes an integrated approach to using remotely sensed data in conjunction with geographic information systems for landscape analysis. Remotely sensed data are compressed into an analytical image-map that is compatible with the most popular geographic information systems as well as freeware viewers. The approach is most effective for landscapes that exhibit a pronounced mosaic pattern of land cover.
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Product Details

  • ISBN-13: 9780387444345
  • Publisher: Springer US
  • Publication date: 10/25/2006
  • Series: Environmental and Ecological Statistics Series , #2
  • Edition description: 2006
  • Edition number: 1
  • Pages: 190
  • Product dimensions: 0.50 (w) x 6.14 (h) x 9.21 (d)

Meet the Author

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.

G.P. Patil: is Distinguished Professor of Mathematical and Environmental Statistics in the Department of Statistics at the Pennsylvania State University, and is a former Visiting Professor of Biostatistics at Harvard University in the Harvard School of Public Health.

He has a Ph.D. in Mathematics, D.Sc. in Statistics, one Honorary Degree in Biological Sciences, and another in Letters. GP is a Fellow of American Statistical Association, Fellow of American Association of Advancement of Science, Fellow of Institute of Mathematical Statistics, Elected Member of the International Statistical Institute, Founder Fellow of the National Institute of Ecology and the Society for Medical Statistics in India.

GP has been a founder of Statistical Ecology Section of International Association for Ecology and Ecological Society of America, a founder of Statistics and Environment Section of American Statistical Association, and a founder of the International Society for Risk Analysis. He is founding editor-in-chief of the international journal, Environmental and Ecological Statistics and founding director of the Penn State Center for Statistical Ecology and Environmental Statistics. He has published thirty volumes and three hundred research papers. GP has received several distinguished awards which include: Distinguished Statistical Ecologist Award of the International Association for Ecology, Distinguished Achievement Medal for Statistics and the Environment of the American Statistical Association, Distinguished Twentieth Century Service Award for Statistical Ecology and Environmental Statistics of the Ninth Lukacs Symposium, Best Paper Award of the American Fisheries Society, and lately, the Best Paper Award of the American Water Resources Association, among others.

Currently, GP is principal investigator of a multi-year NSF grant for surveillance geoinformatics for hotspot detection and prioritization across geographic regions and networks for digital government in the 21st Century.

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Table of Contents


Innovative Imaging, Parsing Patterns and Motivating Models     1
Image Introductory     2
Satellite Sensing Scenario     9
Innovative Imaging of Ecological and Environmental Indicators     11
Georeferencing and Formatting Image Data     16
The 4CS Pattern Perspective On Image Modeling     18
References     21
Pattern Progressions and Segmentation Sequences for Image Intensity Modeling and Grouped Enhancement     23
Pattern Process, Progression, Prominence and Potentials     23
Polypatterns     25
Pattern Pictures, Ordered Overtones and Mosaic Models of Images     26
Pattern Processes for Image Compression by Mosaic Modeling     29
[alpha]-Scenario Starting Stages     31
[alpha]-Scenario Splitting Stage     32
[alpha]-Scenario Shifting Stage     33
[beta]-Scenario Starting Stages     36
[beta]-Scenario Splitting Stage     37
Tree Topology and Level Loss     39
[gamma]-Scenario for Parallel Processing     40
Regional Restoration     42
Relative Residuals     42
Pictorial Presentation and Grouped Versus Global Enhancement     47
Practicalities of Pattern Packages     47
References     48
Collective and Composite Contrast for Pattern Pictures     51
Indirect Imaging by Tabular Transfer     51
Characteristics of Colors     53
Collective Contrast     54
Integrative Image Indicators     55
Composite Contrast for Pattern Pictures     60
Tailored Transfer Tables     61
References     62
Content Classification and Thematic Transforms     63
Interpretive Identification     64
Thematic Transforms     67
Algorithmic Assignments     69
Adaptive Assignment Advisor     70
Mixed Mapping Methods     75
References     78
Comparative Change and Pattern Perturbation     79
Method of Multiple Mappings     80
Compositing Companion Images     81
Direct Difference Detection     82
Pattern Perturbation     87
Integrating Indicators     90
Spanning Three or More Dates     92
References     95
Conjunctive Context     99
Direct Detrending     99
Echelons of Explicit Spatial Structure     103
Disposition and Situation      106
Joint Disposition     106
Edge Affinities     109
Patch Patterns and Generations of Generalization     114
Parquet Polypattern Profiles     115
Conformant/Comparative Contexts and Segment Signal Sequences     117
Principal Properties of Patterns     125
References     128
Advanced Aspects and Anticipated Applications     129
Advantageous Alternative Approaches     129
Structural Sectors of Signal Step Surfaces     131
Thematic Tracking     133
Compositional Components     134
Scale and Scope     136
References     136
Public Packages for Portraying Polypatterns     139
MultiSpec for Multiband Images and Ordered Overtones     139
ArcExplorer     147
[alpha]-Scenario with PSIMAPP Software     149
Polypatterns from Pixels     151
Supplementary Statistics     153
Collective Contrast     153
Tonal Transfer Tables     156
Combinatorial Contrast     159
Regional Restoration     160
Relative Residuals     161
Direct Differences     163
Detecting Changes from Perturbed Patterns     165
Edge Expression     167
Covariance Characteristics     168
Details of Directives for PSIMAPP Modules     171
Glossary     175
Index     177
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