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| Hauptseite > Publikationsdatenbank > Linear and Kernel Methods for Multivariate Change Detection > print |
| Hauptseite > Publikationsdatenbank > Linear and Kernel Methods for Multivariate Change Detection > print |
| 001 | 17745 | ||
| 005 | 20200702121611.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1016/j.cageo.2011.05.012 |
| 024 | 7 | _ | |2 WOS |a WOS:000298524100012 |
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| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 550 |
| 084 | _ | _ | |2 WoS |a Computer Science, Interdisciplinary Applications |
| 084 | _ | _ | |2 WoS |a Geosciences, Multidisciplinary |
| 100 | 1 | _ | |a Canty, M. J. |b 0 |u FZJ |0 P:(DE-Juel1)VDB4989 |
| 245 | _ | _ | |a Linear and Kernel Methods for Multivariate Change Detection |
| 260 | _ | _ | |a Amsterdam [u.a.] |b Elsevier Science |c 2012 |
| 300 | _ | _ | |a 107 - 114 |
| 336 | 7 | _ | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a article |2 DRIVER |
| 440 | _ | 0 | |a Computers & Geosciences |x 0098-3004 |0 19264 |v 38 |
| 500 | _ | _ | |3 POF3_Assignment on 2016-02-29 |
| 500 | _ | _ | |a Record converted from VDB: 12.11.2012 |
| 520 | _ | _ | |a The iteratively reweighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsupervised change detection in multi- and hyperspectral remote sensing imagery and for automatic radiometric normalization of multitemporal image sequences. Principal components analysis (PCA), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving an order of magnitude enhancement in computational speed. The software is available from the authors' Web sites. (C) 2011 Elsevier Ltd. All rights reserved. |
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| 588 | _ | _ | |a Dataset connected to Web of Science |
| 650 | _ | 7 | |a J |2 WoSType |
| 653 | 2 | 0 | |2 Author |a CUDA |
| 653 | 2 | 0 | |2 Author |a ENVI |
| 653 | 2 | 0 | |2 Author |a IDL |
| 653 | 2 | 0 | |2 Author |a IR-MAD |
| 653 | 2 | 0 | |2 Author |a iMAD |
| 653 | 2 | 0 | |2 Author |a Kernel methods |
| 653 | 2 | 0 | |2 Author |a Matlab |
| 653 | 2 | 0 | |2 Author |a Radiometric normalization |
| 653 | 2 | 0 | |2 Author |a Remote sensing |
| 653 | 2 | 0 | |2 Author |a Multiresolution |
| 700 | 1 | _ | |a Nielsen, A.A. |b 1 |0 P:(DE-HGF)0 |
| 773 | _ | _ | |a 10.1016/j.cageo.2011.05.012 |g Vol. 38, p. 107 - 114 |p 107 - 114 |q 38<107 - 114 |0 PERI:(DE-600)1499977-8 |t Computers & geosciences |v 38 |y 2012 |x 0098-3004 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1016/j.cageo.2011.05.012 |
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