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008 120621s2012 xxk| s |||| 0|eng d
020 _a9781447142553
_9978-1-4471-4255-3
024 7 _a10.1007/978-1-4471-4255-3
_2doi
050 4 _aTA1637-1638
050 4 _aTA1637-1638
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aİlsever, Murat.
_eauthor.
245 1 0 _aTwo-Dimensional Change Detection Methods
_h[electronic resource] :
_bRemote Sensing Applications /
_cby Murat İlsever, Cem Ünsalan.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2012.
300 _aX, 72 p. 48 illus., 22 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aIntroduction -- Pixel-Based Change Detection Methods -- Transformation-Based Change Detection Methods -- Structure-Based Change Detection Methods -- Fusion of Change Detection Methods -- Experiments -- Final Comments.
520 _aChange detection using remotely sensed images has many applications, such as urban monitoring, land-cover change analysis, and disaster management. This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. The latter two methods form the basis for a structural analysis of change detection. Three thresholding algorithms are compared, and their effects on the performance of change detection methods are measured. These tests on existing and novel change detection methods make use of a total of 35 panchromatic and multi-spectral Ikonos image sets. Quantitative test results and their interpretations are provided.
650 0 _aComputer science.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
700 1 _aÜnsalan, Cem.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447142546
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4255-3
912 _aZDB-2-SCS
999 _c100781
_d100781