Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis
Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis by Wayne L. Myers, published by Springer US on November 19, 2010, is a softcover reprint of the hardcover first edition from 2006. This book presents a non-conventional approach to multivariate image data analysis, focusing on the systematic application of statistical heuristics to model such data in a quasi-perceptual manner. The work explores how visual scenes are perceived and interpreted by the human eye, employing a method that segments these scenes into hierarchically organized components, referred to as patterns.
Readers will find a detailed examination of the analytical ramifications of this approach, which parallels concepts found in neural networks. The book introduces the acronym PSIMAPP, standing for Progressively Segmented Image Modeling As Poly-Patterns, to formalize its methodology. It discusses the significance of segregated scene segments and their hierarchical organization, which are essential for subsequent statistical analysis. The topics covered include aspects of environmental science, geography, and remote sensing, making it a relevant resource for those interested in the intersection of technology and ecological analysis. With 190 pages, this edition is presented in English and offers insights into the complexities of image analysis and pattern recognition.
Official synopsis Publisher
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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