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Volumn 2015, Issue 1, 2015, Pages

Hyperspectral image classification via contextual deep learning

Author keywords

Contextual deep learning; Hyperspectral image classification; Multinomial logistic regression (MLR); Supervised classification

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA MINING; EXTRACTION; FEATURE EXTRACTION; LEARNING ALGORITHMS; SPECTROSCOPY;

EID: 84938339675     PISSN: 16875176     EISSN: 16875281     Source Type: Journal    
DOI: 10.1186/s13640-015-0071-8     Document Type: Article
Times cited : (140)

References (35)
  • 2
    • 0141934861 scopus 로고    scopus 로고
    • Signal Theory Methods in Multispectral Remote Sensing
    • John Wiley & Sons, Hoboken, New Jersey
    • DA Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, vol. 29 (John Wiley & Sons, Hoboken, New Jersey, 2005).
    • (2005) vol , vol.29
    • Landgrebe, D.A.1
  • 3
    • 4344614511 scopus 로고    scopus 로고
    • Classification of hyperspectral remote sensing images with support vector machines
    • F Melgani, L Bruzzone, Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004). doi:10.1109/TGRS.2004.831865.
    • (2004) IEEE Trans. Geosci. Remote Sens. , vol.42 , Issue.8 , pp. 1778-1790
    • Melgani, F.1    Bruzzone, L.2
  • 5
    • 77957741951 scopus 로고
    • On the mean accuracy of statistical pattern recognizers
    • G Hughes, On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inform. Theory. 14(1), 55–63 (1968). doi:10.1109/TIT.1968.1054102.
    • (1968) IEEE Trans. Inform. Theory. , vol.14 , Issue.1 , pp. 55-63
    • Hughes, G.1
  • 6
    • 20444432773 scopus 로고    scopus 로고
    • Kernel-based methods for hyperspectral image classification
    • G Camps-Valls, L Bruzzone, Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005). doi:10.1109/TGRS.2005.846154.
    • (2005) IEEE Trans. Geosci. Remote Sens. , vol.43 , Issue.6 , pp. 1351-1362
    • Camps-Valls, G.1    Bruzzone, L.2
  • 7
    • 84862993024 scopus 로고    scopus 로고
    • Representative multiple kernel learning for classification in hyperspectral imagery
    • Y Gu, C Wang, D You, Y Zhang, S Wang, Y Zhang, Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 50(7), 2852–2865 (2012). doi:10.1109/TGRS.2005.846154.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , Issue.7 , pp. 2852-2865
    • Gu, Y.1    Wang, C.2    You, D.3    Zhang, Y.4    Wang, S.5    Zhang, Y.6
  • 9
    • 85032751606 scopus 로고    scopus 로고
    • Sparsity and structure in hyperspectral imaging: sensing, reconstruction, and target detection
    • RM Willett, MF Duarte, MA Davenport, RG Baraniuk, Sparsity and structure in hyperspectral imaging: sensing, reconstruction, and target detection. IEEE Signal Process. Mag. 31(1), 116–126 (2014). doi:10.1109/MSP.2013.2279507.
    • (2014) IEEE Signal Process. Mag. , vol.31 , Issue.1 , pp. 116-126
    • Willett, R.M.1    Duarte, M.F.2    Davenport, M.A.3    Baraniuk, R.G.4
  • 10
    • 84900815487 scopus 로고    scopus 로고
    • P Ghamisi, JA Benediktsson, JR Sveinsson, Automatic spectral-spatial classification framework based on attribute profiles and supervised feature extraction. IEEE Trans. Geosci. Remote Sens. 52(9), 5771–5782
    • P Ghamisi, JA Benediktsson, JR Sveinsson, Automatic spectral-spatial classification framework based on attribute profiles and supervised feature extraction. IEEE Trans. Geosci. Remote Sens. 52(9), 5771–5782. doi:10.1109/TGRS.2013.2292544.
  • 11
    • 84905903346 scopus 로고    scopus 로고
    • Automatic framework for spectral-spatial classification based on supervised feature extraction and morphological attribute profiles
    • P Ghamisi, JA Benediktsson, G Cavallaro, A Plaza, Automatic framework for spectral-spatial classification based on supervised feature extraction and morphological attribute profiles. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 7(6), 2147–2160 (2014). doi:10.1109/JSTARS.2014.2298876.
    • (2014) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.7 , Issue.6 , pp. 2147-2160
    • Ghamisi, P.1    Benediktsson, J.A.2    Cavallaro, G.3    Plaza, A.4
  • 12
    • 84897024579 scopus 로고    scopus 로고
    • Supervised segmentation of very high resolution images by the use of extended morphological attribute profiles and a sparse transform
    • J Li, H Zhang, L Zhang, Supervised segmentation of very high resolution images by the use of extended morphological attribute profiles and a sparse transform. IEEE Geosci. Remote Sens. Lett. 11(8), 1409–1413 (2014).
    • (2014) IEEE Geosci. Remote Sens. Lett. , vol.11 , Issue.8 , pp. 1409-1413
    • Li, J.1    Zhang, H.2    Zhang, L.3
  • 14
    • 84921020001 scopus 로고    scopus 로고
    • A survey on spectral-spatial classification techniques based on attribute profiles
    • P Ghamisi, M Dalla Mura, JA Benediktsson, A survey on spectral-spatial classification techniques based on attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(5), 2335–2353 (2015).
    • (2015) IEEE Trans. Geosci. Remote Sens. , vol.53 , Issue.5 , pp. 2335-2353
    • Ghamisi, P.1    Dalla Mura, M.2    Benediktsson, J.A.3
  • 15
    • 84906784859 scopus 로고    scopus 로고
    • Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning
    • Q Zhang, Y Tian, Y Yang, C Pan, Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning. IEEE Trans. Geosci. Remote Sens. 53(1), 261–279 (2015). doi:10.1109/TGRS.2014.2321405.
    • (2015) IEEE Trans. Geosci. Remote Sens. , vol.53 , Issue.1 , pp. 261-279
    • Zhang, Q.1    Tian, Y.2    Yang, Y.3    Pan, C.4
  • 16
    • 85027914747 scopus 로고    scopus 로고
    • Extreme learning machine with composite kernels for hyperspectral image classification
    • Y Zhou, J Peng, C Chen, Extreme learning machine with composite kernels for hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.PP(99), 1–10 (2014).
    • (2014) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.PP , Issue.99 , pp. 1-10
    • Zhou, Y.1    Peng, J.2    Chen, C.3
  • 17
    • 85027908382 scopus 로고    scopus 로고
    • Locality preserving composite kernel feature extraction for multi-source geospatial image analysis
    • Y Zhang, S Prasad, Locality preserving composite kernel feature extraction for multi-source geospatial image analysis. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.PP(99), 1–8 (2014).
    • (2014) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol.PP , Issue.99 , pp. 1-8
    • Zhang, Y.1    Prasad, S.2
  • 18
    • 84871731919 scopus 로고    scopus 로고
    • Hyperspectral image classification via kernel sparse representation
    • Y Chen, NM Nasrabadi, TD Tran, Hyperspectral image classification via kernel sparse representation. IEEE Trans. Geosci. Remote Sens. 51(1), 217–231 (2013). doi:10.1109/TGRS.2012.2201730.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.1 , pp. 217-231
    • Chen, Y.1    Nasrabadi, N.M.2    Tran, T.D.3
  • 19
    • 84872922940 scopus 로고    scopus 로고
    • Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning
    • J Li, JM Bioucas-Dias, A Plaza, Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Trans. Geosci. Remote Sens. 51(2), 844–856 (2013). doi:10.1109/TGRS.2012.2205263.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.2 , pp. 844-856
    • Li, J.1    Bioucas-Dias, J.M.2    Plaza, A.3
  • 20
    • 85032751634 scopus 로고    scopus 로고
    • Advances in hyperspectral image classification: Earth monitoring with statistical learning methods
    • G Camps-Valls, D Tuia, L Bruzzone, J Atli Benediktsson, Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Process. Mag. 31(1), 45–54 (2014). doi:10.1109/MSP.2013.2279179.
    • (2014) IEEE Signal Process. Mag. , vol.31 , Issue.1 , pp. 45-54
    • Camps-Valls, G.1    Tuia, D.2    Bruzzone, L.3    Atli Benediktsson, J.4
  • 21
    • 80052099081 scopus 로고    scopus 로고
    • A spatial-contextual support vector machine for remotely sensed image classification
    • L Cheng-Hsuan, K Bor-Chen, L Chin-Teng, H Chih-Sheng, A spatial-contextual support vector machine for remotely sensed image classification. IEEE Trans. Geosci. Remote Sens. 50(3), 784–799 (2012). doi:10.1109/TGRS.2011.2162246.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , Issue.3 , pp. 784-799
    • Cheng-Hsuan, L.1    Bor-Chen, K.2    Chin-Teng, L.3    Chih-Sheng, H.4
  • 22
    • 84885019653 scopus 로고    scopus 로고
    • Combining support vector machines and Markov random fields in an integrated framework for contextual image classification
    • G Moser, SB Serpico, Combining support vector machines and Markov random fields in an integrated framework for contextual image classification. IEEE Trans. Geosci. Remote Sens. 51(5), 2734–2752 (2013). [doi:110.1109/TGRS.2012.2211882].
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.5 , pp. 2734-2752
    • Moser, G.1    Serpico, S.B.2
  • 23
    • 84947648698 scopus 로고    scopus 로고
    • Land-cover mapping by Markov modeling of spatial-contextual information in very-high-resolution remote sensing images
    • G Moser, SB Serpico, JA Benediktsson, Land-cover mapping by Markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proc. IEEE. 101(3), 631–651 (2013). doi:10.1109/JPROC.2012.2211551.
    • (2013) Proc. IEEE , vol.101 , Issue.3 , pp. 631-651
    • Moser, G.1    Serpico, S.B.2    Benediktsson, J.A.3
  • 25
    • 80053562930 scopus 로고    scopus 로고
    • Hyperspectral image segmentation using a new Bayesian approach with active learning
    • J Li, JM Bioucas-Dias, A Plaza, Hyperspectral image segmentation using a new Bayesian approach with active learning. IEEE Trans. Geosci. Remote Sens. 49(10), 3947–3960 (2011). doi:10.1109/TGRS.2011.2128330.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.10 , pp. 3947-3960
    • Li, J.1    Bioucas-Dias, J.M.2    Plaza, A.3
  • 26
    • 84911126535 scopus 로고    scopus 로고
    • in IEEE Conf. Comput. Vision and Pattern Recognition. Deep learning face representation from predicting 10,000 classes (IEEEColumbus, OH
    • Y Sun, X Wang, X Tang, in IEEE Conf. Comput. Vision and Pattern Recognition. Deep learning face representation from predicting 10,000 classes (IEEEColumbus, OH, USA, 23–28 June 2014), pp. 1891–1898. doi:10.1109/CVPR.2014.244.
    • (2014) USA , vol.23-28 , pp. 1891-1898
    • Sun, Y.1    Wang, X.2    Tang, X.3
  • 27
    • 84938391768 scopus 로고    scopus 로고
    • L Deng, GE Dahl, in NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. Roles of pre-training and fine-tuning in context-dependent DBN-HMMs for real-world speech recognition (Whistler, BC
    • D Yu, L Deng, GE Dahl, in NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. Roles of pre-training and fine-tuning in context-dependent DBN-HMMs for real-world speech recognition (Whistler, BC, Canada, 10 December 2010).
    • (2010) Canada , pp. 10
  • 28
    • 84908032942 scopus 로고    scopus 로고
    • F Zhang, B Du, L Zhang, Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175–2184. IEEE
    • F Zhang, B Du, L Zhang, Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175–2184. IEEE.
  • 29
    • 84890425279 scopus 로고    scopus 로고
    • AM Cheriyadat, Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451
    • AM Cheriyadat, Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451. doi:10.1109/TGRS.2013.2241444.
  • 33
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • GE Hinton, S Osindero, Y-W Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). doi:10.1162/neco.2006.18.7.1527.
    • (2006) Neural Comput. , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 35
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion
    • P Vincent, H Larochelle, I Lajoie, Y Bengio, P-A Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010).
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 3371-3408
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.-A.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.