000 03941nam a22005415i 4500
001 978-3-642-14212-3
003 DE-He213
005 20140220083745.0
007 cr nn 008mamaa
008 110322s2011 gw | s |||| 0|eng d
020 _a9783642142123
_9978-3-642-14212-3
024 7 _a10.1007/978-3-642-14212-3
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aPrasad, Saurabh.
_eeditor.
245 1 0 _aOptical Remote Sensing
_h[electronic resource] :
_bAdvances in Signal Processing and Exploitation Techniques /
_cedited by Saurabh Prasad, Lori M. Bruce, Jocelyn Chanussot.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aVIII, 344 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAugmented Vision and Reality ;
_v3
505 0 _apre-processing images -- storing and representing high dimensional data -- fusing different sensor modalities -- pattern classification and target recognition -- visualization of high dimensional imagery.
520 _aOptical remote sensing involves acquisition and analysis of optical data – electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground.  Optical image acquisition modalities have come a long way – from gray-scale photogrammetric images to hyperspectral images. The advances in imaging hardware over recent decades have enabled availability of high spatial, spectral and temporal resolution imagery to the remote sensing analyst. These advances have created unique challenges for researchers in the remote sensing community working on algorithms for representation, exploitation and analysis of such data. Early optical remote sensing systems relied on multispectral sensors, which are characterized by a small number of wide spectral bands. Although multispectral sensors are still employed by analysts, in recent years, the remote sensing community has seen a steady shift to hyperspectral sensors, which are characterized by hundreds of fine resolution co-registered spectral bands, as the dominant optical sensing technology. Such data has the potential to reveal the underlying phenomenology as described by spectral characteristics accurately. This “extension” from multispectral to hyperspectral imaging does not imply that the signal processing and exploitation techniques can be simply scaled up to accommodate the extra dimensions in the data. This book presents state-of-the-art signal processing and exploitation algorithms that address three key challenges within the context of modern optical remote sensing: (1) Representation and visualization of high dimensional data for efficient and reliable transmission, storage and interpretation; (2) Statistical pattern classification for robust land-cover-classification, target recognition and pixel unmixing; (3) Fusion of multi-sensor data to effectively exploit multiple sources of information for analysis.
650 0 _aEngineering.
650 0 _aOptical pattern recognition.
650 0 _aMicrowaves.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aPattern Recognition.
650 2 4 _aMicrowaves, RF and Optical Engineering.
700 1 _aBruce, Lori M.
_eeditor.
700 1 _aChanussot, Jocelyn.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642142116
830 0 _aAugmented Vision and Reality ;
_v3
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-14212-3
912 _aZDB-2-ENG
999 _c106935
_d106935