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999			_c33705 _d33705
001			on1089930835
003			OCoLC
005			20200702121257.0
006			m d
007			cr cnu|||unuuu
008			190315s2019 flu ob 001 0 eng d
020			_a9780429875359 _q(electronic bk.)
020			_a0429875355 _q(electronic bk.)
020			_a9780429464348 _q(electronic bk.)
020			_a0429464347 _q(electronic bk.)
020			_a9780429875335 _q(electronic bk. : Mobipocket)
020			_a0429875339 _q(electronic bk. : Mobipocket)
020			_a9780429875342 _q(electronic bk. : EPUB)
020			_a0429875347 _q(electronic bk. : EPUB)
020			_z9781138613225
020			_z1138613223
035			_a2053681
035			_a(OCoLC)1089930835 _z(OCoLC)1090011154
040			_aN$T _beng _erda _epn _cN$T _dN$T _dYDX _dTYFRS
049			_aMAIN
050		4	_aG70.4
072		7	_aTEC _x009070 _2bisacsh
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072		7	_aSCI _x026000 _2bisacsh
072		7	_aSCI _x019000 _2bisacsh
072		7	_aRGC _2bicssc
082	0	4	_a621.36/70285 _223
100	1		_aCanty, Morton John.
245	1	0	_aImage Analysis, Classification and Change Detection in Remote Sensing : _bWith Algorithms for Python / _cMorton John Canty.
250			_aFourth edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2019.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_aImage Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty's fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.
545	0		_aMorton John Canty is a senior research scientist in the Institute for Bio- and Geosciences at the Juelich Research Center in Germany, now semi-retired. He received his PhD in Nuclear Physics in 1969 at the University of Manitoba, Canada and, after post-doctoral positions in Bonn, Groningen and Marburg, began work in Juelich in 1979. There, his principal interests have been the development of statistical and gametheoretical models for the verification of international treaties and the use of remote sensing data for monitoring global treaty compliance. He has served on numerous advisory bodies to the German federal government and to the International Atomic Energy Agency in Vienna and was a coordinator within the European Network of Excellence on Global Monitoring for Security and Stability, funded by the European Commission. Morton Canty is the author of three monographs in the German language: on the subject of non-linear dynamics (Chaos und Systeme, Vieweg, 1995), neural networks for classification of remote sensing data (Fernerkundung mit neuronalen Netzen, Expert, 1999) and algorithmic game theory (Konfliktl¨osungen mit Mathematica, Springer 2000). The latter text has appeared in a revised English version (Resolving Conflicts withMathematica, Academic Press, 2003). He is co-author of a monograph on mathematical methods for treaty verification (Compliance Quantified, Cambridge University Press, 1996). He has published many papers on the subjects of experimental nuclear physics, nuclear safeguards, applied game theory and remote sensing. He has lectured on nonlinear dynamical growth models and remote sensing digital image analysis to students at both the graduate and undergraduate level at Universities in Bonn, Berlin, Freiberg/Saxony and Rome.
588	0		_aOnline resource; title from PDF file page (EBSCO, viewed March 18, 2019).
590			_aMaster record variable field(s) change: 072
650		0	_aRemote sensing _xMathematics.
650		0	_aImage analysis _xMathematics.
650		0	_aImage analysis _xData processing.
650		0	_aPython (Computer program language)
650		7	_aTECHNOLOGY & ENGINEERING / Mechanical. _2bisacsh
650		7	_aTECHNOLOGY / Environmental Engineering & Technology _2bisacsh
650		7	_aSCIENCE / Environmental Science _2bisacsh
650		7	_aSCIENCE / Earth Sciences / General _2bisacsh
655		4	_aElectronic books.
776	0	8	_iPrint version: _z9781138613225 _z1138613223 _w(DLC) 2018051975 _w(OCoLC)1076372553
850			_aNMUCL
856	4	0	_3EBSCOhost _uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2053681
942			_2nlm _cEBK