000 | 03479cam a2200613 i 4500 | ||
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001 | 9781003020851 | ||
003 | FlBoTFG | ||
005 | 20220414100316.0 | ||
006 | m d | | | ||
007 | cr ||||||||||| | ||
008 | 200524s2020 flua ob 001 0 eng | ||
040 |
_aOCoLC-P _beng _erda _cOCoLC-P |
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020 |
_a9781000093599 _qelectronic book |
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020 |
_a100009359X _qelectronic book |
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020 |
_a9781003020851 _qelectronic book |
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020 |
_a1003020852 _qelectronic book |
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020 |
_z9780367858483 _qhardcover |
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020 |
_a9781000093612 _q(ePub ebook) |
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020 |
_a1000093611 _q(ePub ebook) |
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020 |
_a9781000093605 _q(Mobipocket ebook) |
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020 |
_a1000093603 _q(Mobipocket ebook) |
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024 | 7 |
_a10.1201/9781003020851 _2doi |
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035 |
_a(OCoLC)1158508782 _z(OCoLC)1173644635 |
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035 | _a(OCoLC-P)1158508782 | ||
050 | 0 | 4 |
_aG70.4 _b.C75 2020 |
072 | 7 |
_aTEC _x036000 _2bisacsh |
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072 | 7 |
_aTEC _x015000 _2bisacsh |
|
072 | 7 |
_aCOM _x012050 _2bisacsh |
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072 | 7 |
_aRGW _2bicssc |
|
082 | 0 | 0 |
_a621.36/78 _223 |
100 | 1 |
_aCresson, Rémi, _eauthor. |
|
245 | 1 | 0 |
_aDeep learning for remote sensing images with open source software / _cRémi Cresson. |
250 | _aFirst edition. | ||
264 | 1 |
_aBoca Raton, FL : _bCRC Press, Taylor & Francis Group, _c[2020] |
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300 |
_a1 online resource (xi, 151 pages) : _bcolor illustrations. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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490 | 0 | _aSignal and image processing of Earth observation series | |
505 | 0 | _aDeep learning backgrounds -- Software -- Data used : the Tokyo dataset -- A simple convolutional neural network -- Fully convolutional neural network -- Classifiers on deep features -- Dealing with multiple sources -- Semantic segmentation of optical imagery -- Data used : the Amsterdam dataset -- Mapping buildings -- Gap filling of optical images : principle -- The Marmande dataset -- Pre-processing -- Model training -- Inference. | |
520 |
_a"In today's world, deep learning source codes and a plethora of open access geospatial images are available, but readers are missing the educational tools. This is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches are generic and adapted to suit many applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps readers link together the theory and practical use of existing tools and data to create their own remote sensing data processing"-- _cProvided by publisher. |
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588 | _aOCLC-licensed vendor bibliographic record. | ||
650 | 0 |
_aRemote sensing _xData processing. |
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650 | 0 | _aRemote-sensing images. | |
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aMachine learning. | |
650 | 0 | _aNeural networks (Computer science) | |
650 | 0 | _aOpen source software. | |
650 | 7 |
_aTECHNOLOGY / Remote Sensing _2bisacsh |
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650 | 7 |
_aTECHNOLOGY / Imaging Systems _2bisacsh |
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650 | 7 |
_aCOMPUTERS / Computer Graphics / Image Processing (see also PHOTOGRAPHY / Techniques / Digital) _2bisacsh |
|
856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003020851 |
856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
999 |
_c58942 _d58942 |