| 書(shū)目名稱 | Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis |
| 編輯 | Wayne L. Myers,Ganapati P. Patil |
| 視頻video | http://file.papertrans.cn/743/742700/742700.mp4 |
| 概述 | Presents a non-conventional approach to multivariate image-structured data.Includes supplementary material: |
| 叢書(shū)名稱 | Environmental and Ecological Statistics |
| 圖書(shū)封面 |  |
| 描述 | 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 |
| 出版日期 | Book 2006 |
| 關(guān)鍵詞 | GIS; Geoinformationssysteme; classification; distribution; formation; geography; landscape analysis; modeli |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-44439-0 |
| isbn_softcover | 978-1-4419-4271-5 |
| isbn_ebook | 978-0-387-44439-0Series ISSN 2363-9660 Series E-ISSN 2363-9679 |
| issn_series | 2363-9660 |
| copyright | Springer-Verlag US 2006 |
|Archiver|手機(jī)版|小黑屋|
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