000			03349cam a2200529 i 4500
001			9781003373216
003			FlBoTFG
005			20240213122834.0
006			m o d
007			cr cnu---unuuu
008			230419s2023 xx o 0|1 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003373216 _q(electronic bk.)
020			_a1003373216 _q(electronic bk.)
020			_a9781000872194 _q(electronic bk. : PDF)
020			_a100087219X _q(electronic bk. : PDF)
020			_a9781000872200 _q(electronic bk. : EPUB)
020			_a1000872203 _q(electronic bk. : EPUB)
020			_z1032428325
020			_z9781032428321
024	7		_a10.1201/9781003373216 _2doi
035			_a(OCoLC)1376455086
035			_a(OCoLC-P)1376455086
050		4	_aG70.4
072		7	_aCOM _x037000 _2bisacsh
072		7	_aTEC _x010000 _2bisacsh
072		7	_aTEC _x036000 _2bisacsh
072		7	_aRGW _2bicssc
082	0	4	_a621.3678 _223/eng/20230501
100	1		_aKumar, Anil _c(Engineer), _eauthor.
245	1	0	_aMulti-sensor and multi-temporal remote sensing : _bspecific single class mapping.
264		1	_a[Place of publication not identified] : _bCRC Press, _c2023.
300			_a1 online resource (184 pages) : _billustrations (black and white).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aThis book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the individual sample as mean' training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields. Key features: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aRemote sensing.
650		7	_aCOMPUTERS / Machine Theory _2bisacsh
650		7	_aTECHNOLOGY / Environmental Engineering & Technology _2bisacsh
650		7	_aTECHNOLOGY / Remote Sensing _2bisacsh
700	1		_aUpadhyay, Priyadarshi, _eauthor.
700	1		_aSingh, Uttara, _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003373216
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c6282 _d6282