-
1
-
-
84855458471
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
12
-
-
84929407972
-
Decision fusion for hyperspectral image classification based on minimum-distance classifiers in the wavelet domain
-
Xi'an, China, 9-13 July 2014 IEEE: Xi'an, China
-
Li,W.; Prasad, S.; Tramel, E.W.; Fowler, J.E.; Du, Q. Decision fusion for hyperspectral image classification based on minimum-distance classifiers in the wavelet domain. In Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi'an, China, 9-13 July 2014; IEEE: Xi'an, China, 2014; pp. 162-165
-
(2014)
Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)
, pp. 162-165
-
-
Li, W.1
Prasad, S.2
Tramel, E.W.3
Fowler, J.E.4
Du, Q.5
-
13
-
-
0036403150
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
20
-
-
24644496298
-
Local discriminant embedding and its variants
-
San Diego, CA, USA, 20-25 June
-
Chen, H.T.; Chang, H.W.; Liu, T.L. Local discriminant embedding and its variants. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 20-25 June 2005; pp. 846-853
-
(2005)
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
, pp. 846-853
-
-
Chen, H.T.1
Chang, H.W.2
Liu, T.L.3
-
21
-
-
34249086815
-
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
-
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
-
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
-
24
-
-
70350487954
-
Dimensionality reduction: A comparative
-
Van Der Maaten, L.; Postma, E.; Van den Herik, J. Dimensionality reduction: A comparative. J. Mach. Learn. Res. 2009, 10, 66-71
-
(2009)
J. Mach. Learn. Res
, vol.10
, pp. 66-71
-
-
Van Der Maaten, L.1
Postma, E.2
Van den Herik, J.3
-
25
-
-
84888349041
-
Hyperspectral remote sensing data analysis and future challenges
-
Bioucas-Dias, J.M.; Plaza, A.; Camps-Valls, G.; Scheunders, P.; Nasrabadi, N.; Chanussot, J. Hyperspectral remote sensing data analysis and future challenges. IEEE Trans. Geosci. Remote Sens. M. 2013, 1, 6-36
-
(2013)
IEEE Trans. Geosci. Remote Sens. M
, vol.1
, pp. 6-36
-
-
Bioucas-Dias, J.M.1
Plaza, A.2
Camps-Valls, G.3
Scheunders, P.4
Nasrabadi, N.5
Chanussot, J.6
-
26
-
-
84877927306
-
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
-
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
-
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
-
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
-
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
-
-
84952685590
-
Deep learning-based man-made object detection from hyperspectral data
-
Makantasis, K.; Karantzalos, K.; Doulamis, A.; Loupos, K. Deep learning-based man-made object detection from hyperspectral data. Lec. Notes Comput. Sc. 2015, 9474, 717-727
-
(2015)
Lec. Notes Comput. Sc
, vol.9474
, pp. 717-727
-
-
Makantasis, K.1
Karantzalos, K.2
Doulamis, A.3
Loupos, K.4
-
33
-
-
84959139728
-
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
-
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
-
35
-
-
71149119164
-
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
-
Montreal, Canada, 14-18 June
-
Lee, H.; Grosse, R.; Ranganath, R.; Ng, A.Y. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the Appearing in Proceedings of the 26th International Conference on Machine Learning, Montreal, Canada, 14-18 June 2009
-
(2009)
Proceedings of the Appearing in Proceedings of the 26th International Conference on Machine Learning
-
-
Lee, H.1
Grosse, R.2
Ranganath, R.3
Ng, A.Y.4
-
37
-
-
84919775831
-
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
-
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
-
Intraclass correlation coefficient
-
Sedgwick, P. Intraclass correlation coefficient. BMJ 2013, 346, 1816
-
(2013)
BMJ
, vol.346
, pp. 1816
-
-
Sedgwick, P.1
-
40
-
-
56449086627
-
Sparse deep belief net model for visual area V2
-
Curran Associates Inc.: New York, NY USA
-
Lee, H.; Ekanadham, C.; Ng, A.Y. Sparse deep belief net model for visual area V2. In International Conference on Neural Information Processing Systems; Curran Associates Inc.: New York, NY USA, 2007; pp. 873-880
-
(2007)
International Conference on Neural Information Processing Systems
, pp. 873-880
-
-
Lee, H.1
Ekanadham, C.2
Ng, A.Y.3
-
41
-
-
85021799578
-
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
-
42
-
-
84965140688
-
Learning both weights and connections for efficient neural networks
-
Montreal, QC, Canada, 7-12 December
-
Han, S.; Pool, J.; Tran, J.; Dally, W.J. Learning both weights and connections for efficient neural networks. In Proceedings of the NIPS'15 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 7-12 December 2015; Volume 1, pp. 1135-1143
-
(2015)
InProceedings of the NIPS'15 28th International Conference on Neural Information Processing Systems
, vol.1
, pp. 1135-1143
-
-
Han, S.1
Pool, J.2
Tran, J.3
Dally, W.J.4
-
43
-
-
84906303790
-
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
|