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

By: Wayne L. Myers, Ganapati P. Patil

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Hardcover | 11 December 2006

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208 Pages

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23.5 x 15.88 x 1.27

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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.

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

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

At a Glance

Hardcover

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