04809nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137040000900172082001400181100002900195245014400224264003800368300003500406336002600441337002600467338003900493347002400532490004900556505039500605520282801000650002803828650002003856650002303876650001603899650001703915650002603932650003503958650002303993650006104016700003204077710003404109773002004143776003604163830004904199856010304248978-0-387-44439-0DE-He21320260521091916.0cr nn 008mamaa100301s2006    xxu|    s    |||| 0|eng d  a9780387444390  a997803874443907 a10.1007/978-0-387-44439-02doi  cCICY04a333.72231 aMyers, Wayne L.eauthor.10aPattern-Based Compression of Multi-Band Image Data for Landscape Analysish[recurso electrónico] /cby Wayne L. Myers, Ganapati P. Patil. 1aBoston, MA :bSpringer US,c2006.  aXVII, 186 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aEnvironmental and Ecological Statistics ;v20 aInnovative Imaging, Parsing Patterns and Motivating Models -- Pattern Progressions and Segmentation Sequences for IMAGE Intensity Modeling and Grouped Enhancement -- Collective and Composite Contrast for Pattern Pictures -- Content Classification and Thematic Transforms -- Comparative Change and Pattern Perturbation -- Conjunctive Context -- Advanced Aspects and Anticipated Applications.  aPattern-Based Compression of Multi-Band Image Data for Landscape Analysis describes an integrated approach to using remotely sensed data in conjunction with geographic information systems (GIS) for landscape analysis. Remotely sensed data are compressed by compound segmentation so that the first level is an image-like raster map for GIS, and a second level affords approximate restoration. Pattern processing is implemented in software by PSIMAPP Progressively Segmented Image Modeling As Poly-Patterns. 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 difference 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 several scales. 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. 0aENVIRONMENTAL SCIENCES. 0aREMOTE SENSING. 0aLANDSCAPE ECOLOGY. 0aSTATISTICS.14aENVIRONMENT.24aENVIRONMENT, GENERAL.24aREMOTE SENSING/PHOTOGRAMMETRY.24aLANDSCAPE ECOLOGY.24aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.1 aPatil, Ganapati P.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387444345 0aEnvironmental and Ecological Statistics ;v240uhttp://dx.doi.org/10.1007/978-0-387-44439-0zVer el texto completo en las instalaciones del CICY