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Overview
There are seven notable areas of advantage in this approach: Controlling and creating contrast for pictorial presentations. Classifying content for constructing categorical maps. Whereas digital image data are usually directed toward algorithmic assignment of image elements to candidate categories of content, this approach is equally applicable to assisting interactive interpretive assignment by a human analyst. Detecting differences between instances of imaging. Whereas conventional change detection is done in the signal domain, this approach supports dual pattern matching in signal and spatial domains. Advantage in contextual considerations. Having parsed patterns into collective components allows analysts to conduct comparatives in multiple modes. The components can be combined according to signal similarities and proximate positioning to generate generalized images that portray progressively more prominent patterning. The patterns can be treated as multivariate trends for removal to reach residuals that are regionalized in accordance with scenarios of spatial statistics. An entirely new arena of analysis is posed by pattern profiles of cumulated components over blocks at severalscales. Compositional components of complexes can be considered in terms of chromaticity or ratio relations among signal sets by partial ordering and rank range runs. Informational compression for conveyance by computer media. The poly-pattern models occupy the equivalent of two single-byte signal bands along with tables of pattern properties. Although approximation in restoration might appear to be a drawback, it leads to the sixth aspect of advantage. Digital image data are often proprietary with strictures on distribution. Since the poly-pattern models do not provide capability for complete restoration, and in view of their numerous advantages, they become substantially different derivative products in much the same manner as a thematic map. Therefore, most of the proprietary concerns relative to the original data should be obviated. The interface between image analysis and GIS. GIS provides the popular platform for utilization of geo-spatial information. Since relatively few of the regular GIS users are image analysts, poly-pattern packaging facilitates broader access to image-based information.
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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.
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