메뉴 건너뛰기




Volumn 11, Issue 2, 2019, Pages

An improved model based detection of urban impervious surfaces using multiple features extracted from ROSIS-3 hyperspectral images

Author keywords

Deep learning; Dimensionality reduction; Hyperspectral images; Multi feature extraction; Urban impervious surface

Indexed keywords

DEEP LEARNING; EXTRACTION; FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; REGRESSION ANALYSIS; SPECTROSCOPY; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

EID: 85060693841     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs11020136     Document Type: Article
Times cited : (9)

References (43)
  • 1
    • 84855458471 scopus 로고    scopus 로고
    • Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
    • Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34-49
    • (2012) Remote Sens. Environ , vol.117 , pp. 34-49
    • Weng, Q.1
  • 2
    • 85040825988 scopus 로고    scopus 로고
    • Spatial and temporal variation of the urban impervious surface and its driving forces in the central city of Harbin
    • Li, M.; Zang, S.; Wu, C.; Na, X. Spatial and temporal variation of the urban impervious surface and its driving forces in the central city of Harbin. J. Geogr. Sci. 2018, 28, 323-336
    • (2018) J. Geogr. Sci , vol.28 , pp. 323-336
    • Li, M.1    Zang, S.2    Wu, C.3    Na, X.4
  • 3
    • 85032751896 scopus 로고    scopus 로고
    • Hyperspectral image data analysis
    • Landgrebe, D. Hyperspectral image data analysis. IEEE Signal Process Mag. 2002, 19, 17-28
    • (2002) IEEE Signal Process Mag , vol.19 , pp. 17-28
    • Landgrebe, D.1
  • 4
    • 85027942618 scopus 로고    scopus 로고
    • Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network
    • Chen, Y.; Zhao, X.; Jia, X. Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network. IEEE J-Stars 2015, 8, 2381-2392
    • (2015) IEEE J-Stars , vol.8 , pp. 2381-2392
    • Chen, Y.1    Zhao, X.2    Jia, X.3
  • 5
    • 85003666734 scopus 로고    scopus 로고
    • Deep LearningWith Grouped Features for Spatial Spectral Classification of Hyperspectral Images
    • Zhou, X.; Li, S.; Tang, F.; Qin, K.; Hu, S.; Liu, S. Deep LearningWith Grouped Features for Spatial Spectral Classification of Hyperspectral Images. IEEE Geosci. Remote Sens. Lett. 2017, 14, 97-101
    • (2017) IEEE Geosci. Remote Sens. Lett , vol.14 , pp. 97-101
    • Zhou, X.1    Li, S.2    Tang, F.3    Qin, K.4    Hu, S.5    Liu, S.6
  • 6
    • 80052087210 scopus 로고    scopus 로고
    • On combining multiple features for hyperspectral remote sensing image classification
    • Zhang, L.; Zhang, L.; Tao, D.; Huang, X. On combining multiple features for hyperspectral remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 2012, 50, 879-893
    • (2012) IEEE Trans. Geosci. Remote Sens , vol.50 , pp. 879-893
    • Zhang, L.1    Zhang, L.2    Tao, D.3    Huang, X.4
  • 7
    • 84905925092 scopus 로고    scopus 로고
    • Deep learning-based classification of hyperspectral data
    • Chen, Y.; Lin, Z.; Zhao, X.; Wang, G.; Gu, Y. Deep learning-based classification of hyperspectral data. IEEE J-STARS 2014, 7, 2094-2107
    • (2014) IEEE J-STARS , vol.7 , pp. 2094-2107
    • Chen, Y.1    Lin, Z.2    Zhao, X.3    Wang, G.4    Gu, Y.5
  • 8
    • 0036762725 scopus 로고    scopus 로고
    • Spatial/spectral endmember extraction by multidimensional morphological operations
    • Plaza, A.; Martinez, P.; Perez, R.; Plaza, J. Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2025-2041
    • (2002) IEEE Trans. Geosci. Remote Sens , vol.40 , pp. 2025-2041
    • Plaza, A.1    Martinez, P.2    Perez, R.3    Plaza, J.4
  • 9
    • 70349174319 scopus 로고    scopus 로고
    • The influence of urban structures on impervious surface maps from airborne hyperspectral data
    • Linden, S.V.D.; Hostert, P. The influence of urban structures on impervious surface maps from airborne hyperspectral data. Remote Sens. Environ. 2009, 113, 2298-2305
    • (2009) Remote Sens. Environ , vol.113 , pp. 2298-2305
    • Linden, S.V.D.1    Hostert, P.2
  • 10
    • 85041795970 scopus 로고    scopus 로고
    • Urban impervious surface extraction from very high resolution imagery using spatial and spectral unmixing and decision tree method
    • Li, P.; Chen, Y. Urban impervious surface extraction from very high resolution imagery using spatial and spectral unmixing and decision tree method. IEEE Geosci. Remote Sens. Symp. 2017, 6287-6289
    • (2017) IEEE Geosci. Remote Sens. Symp , pp. 6287-6289
    • Li, P.1    Chen, Y.2
  • 11
    • 84931348274 scopus 로고    scopus 로고
    • Spatial preprocessing based multinomial logistic regression for hyperspectral image classification
    • Prabhakar, T.V.N.; Xavier, G.; Geetha, P.; Soman, K.P. Spatial preprocessing based multinomial logistic regression for hyperspectral image classification. Procedia Comp. Sci. 2015, 46, 1817-1826
    • (2015) Procedia Comp. Sci , vol.46 , pp. 1817-1826
    • Prabhakar, T.V.N.1    Xavier, G.2    Geetha, P.3    Soman, K.P.4
  • 13
    • 0036403150 scopus 로고    scopus 로고
    • Simplified maximum likelihood classification for hyperspectral data in cluster space
    • Jia, X. Simplified maximum likelihood classification for hyperspectral data in cluster space. IEEE Geosci. Remote Sens. Symp. 2002, 5, 2578-2580
    • (2002) IEEE Geosci. Remote Sens. Symp , vol.5 , pp. 2578-2580
    • Jia, X.1
  • 14
    • 85060693952 scopus 로고    scopus 로고
    • An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Regions in Hyperspectral Data via Non-Linear Dimensionality Reduction
    • Perumal, K.; Bhaskaran, R. An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Regions in Hyperspectral Data via Non-Linear Dimensionality Reduction. IJSIP 2010, 1, 1-8
    • (2010) IJSIP , vol.1 , pp. 1-8
    • Perumal, K.1    Bhaskaran, R.2
  • 15
    • 85007425333 scopus 로고    scopus 로고
    • The airborne hyperspectral image classification based on the random forest algorithm
    • Wang, S.; Dou, A.; Yuan, X.; Zhang, X. The airborne hyperspectral image classification based on the random forest algorithm. IEEE Geosci. Remote Sens. Symp. 2016, 2280-2283
    • (2016) IEEE Geosci. Remote Sens. Symp , pp. 2280-2283
    • Wang, S.1    Dou, A.2    Yuan, X.3    Zhang, X.4
  • 16
    • 4344614511 scopus 로고    scopus 로고
    • Classification of hyperspectral remote sensing images with support vector machines
    • Melgani, F.; Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1778-1790
    • (2004) IEEE Trans. Geosci. Remote Sens , vol.42 , pp. 1778-1790
    • Melgani, F.1    Bruzzone, L.2
  • 17
    • 84880277002 scopus 로고    scopus 로고
    • Combining multiple classification methods for hyperspectral data interpretation
    • Santos, A.B.; Araújo, A.D.A.; Menotti, D. Combining multiple classification methods for hyperspectral data interpretation. IEEE J-STARS 2013, 6, 1450-1459
    • (2013) IEEE J-STARS , vol.6 , pp. 1450-1459
    • Santos, A.B.1    Araújo, A.D.A.2    Menotti, D.3
  • 18
    • 84867975740 scopus 로고    scopus 로고
    • A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy
    • Yin, J.;Wang, Y.; Hu, J. A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy. IEEE Trans. Ind. Inf. 2012, 8, 935-943
    • (2012) IEEE Trans. Ind. Inf , vol.8 , pp. 935-943
    • Yin, J.1    Wang, Y.2    Hu, J.3
  • 19
    • 14644435059 scopus 로고    scopus 로고
    • Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations
    • Plaza, A.; Martinez, P.; Plaza, J.; Perez, R. Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans. Geosci. Remote Sens. 2005, 43, 466-479
    • (2005) IEEE Trans. Geosci. Remote Sens , vol.43 , pp. 466-479
    • Plaza, A.1    Martinez, P.2    Plaza, J.3    Perez, R.4
  • 21
    • 34249086815 scopus 로고    scopus 로고
    • Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis
    • Sugiyama, M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J. Mach. Learn. Res. 2007, 8, 1027-1061
    • (2007) J. Mach. Learn. Res , vol.8 , pp. 1027-1061
    • Sugiyama, M.1
  • 22
    • 57249084011 scopus 로고    scopus 로고
    • Visualizing data using t-SNE
    • Maaten, L.; Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 2009, 9, 2579-2605
    • (2009) J. Mach. Learn. Res , vol.9 , pp. 2579-2605
    • Maaten, L.1    Hinton, G.2
  • 23
    • 0036165146 scopus 로고    scopus 로고
    • On a connection between Kernel PCA and metric multidimensional scaling
    • Williams, C.K.I. On a connection between Kernel PCA and metric multidimensional scaling. Mach. Learn. 2002, 46, 11-19
    • (2002) Mach. Learn , vol.46 , pp. 11-19
    • Williams, C.K.I.1
  • 26
    • 84877927306 scopus 로고    scopus 로고
    • Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation
    • Qian, Y.; Ye, M. Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation. IEEE J-Stars 2013, 6, 499-515
    • (2013) IEEE J-Stars , vol.6 , pp. 499-515
    • Qian, Y.1    Ye, M.2
  • 27
    • 84979492674 scopus 로고    scopus 로고
    • Spectral-spatial feature extraction for hyperspectral image classification: A Dimension Reduction and Deep Learning Approach
    • Zhao, W.; Du, S. Spectral-spatial feature extraction for hyperspectral image classification: A Dimension Reduction and Deep Learning Approach. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4544-4554
    • (2016) IEEE Trans. Geosci. Remote Sens , vol.54 , pp. 4544-4554
    • Zhao, W.1    Du, S.2
  • 28
    • 0034890711 scopus 로고    scopus 로고
    • An improved model for automatic feature-based registration of SAR and SPOT images
    • Dare, P.; Dowman, I. An improved model for automatic feature-based registration of SAR and SPOT images. Isprs 2002, 56, 13-28
    • (2002) Isprs , vol.56 , pp. 13-28
    • Dare, P.1    Dowman, I.2
  • 29
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G.E.; Salakhutdinov, R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504-507. [PubMed]
    • (2006) Science , vol.313 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 31
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; Manzagol, P.A. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 2010, 11, 3371-3408
    • (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
  • 32
  • 33
    • 84959139728 scopus 로고    scopus 로고
    • Spectral-spatial classification of hyperspectral image based on deep auto-encoder
    • Ma, X.; Wang, H.; Geng, J. Spectral-spatial classification of hyperspectral image based on deep auto-encoder. IEEE J-Stars 2016, 9, 4073-4085
    • (2016) IEEE J-Stars , vol.9 , pp. 4073-4085
    • Ma, X.1    Wang, H.2    Geng, J.3
  • 34
    • 85016393818 scopus 로고    scopus 로고
    • Learning to diversify deep belief networks for hyperspectral image classification
    • Zhong, P.; Gong, Z.; Li, S.; Schönlieb, C.B. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2017, 55, 3516-3530
    • (2017) IEEE Trans. Geosci. Remote Sens , vol.55 , pp. 3516-3530
    • Zhong, P.1    Gong, Z.2    Li, S.3    Schönlieb, C.B.4
  • 37
    • 84919775831 scopus 로고    scopus 로고
    • Accelerating t-sne using tree-based algorithms
    • Laurens VD, M. Accelerating t-sne using tree-based algorithms. J. Mach. Learn. Res. 2014, 15, 3221-3245
    • (2014) J. Mach. Learn. Res , vol.15 , pp. 3221-3245
    • Laurens VD, M.1
  • 38
    • 33846349887 scopus 로고
    • A hierarchical o(n log n) force-calculation algorithm
    • Barnes, J.; Hut, P. A hierarchical o(n log n) force-calculation algorithm. Nature 1986, 324, 446-449
    • (1986) Nature , vol.324 , pp. 446-449
    • Barnes, J.1    Hut, P.2
  • 39
    • 84875763974 scopus 로고    scopus 로고
    • Intraclass correlation coefficient
    • Sedgwick, P. Intraclass correlation coefficient. BMJ 2013, 346, 1816
    • (2013) BMJ , vol.346 , pp. 1816
    • Sedgwick, P.1
  • 41
    • 85021799578 scopus 로고    scopus 로고
    • Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
    • Han, S.; Mao, H.; Dally, W.J. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber 2016, 56, 3-7
    • (2016) Fiber , vol.56 , pp. 3-7
    • Han, S.1    Mao, H.2    Dally, W.J.3
  • 43
    • 84906303790 scopus 로고    scopus 로고
    • Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification
    • Zhou, Y.; Peng, J.; Chen, C. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2015, 53, 1082-1095
    • (2015) IEEE Trans. Geosci. Remote Sens , vol.53 , pp. 1082-1095
    • Zhou, Y.1    Peng, J.2    Chen, C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.