Infrared Technology, Volume. 42, Issue 9, 855(2020)
Hyperspectral Image Classification Algorithm Based on Multiscale Convolutional Neural Network
[1] [1] LI Shutao, SONG Weiwei, FANG Leyuan, et al. Deep Learning for Hyperspectral Image Classification: An Overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6690-6709.
LI Shutao, SONG Weiwei, FANG Leyuan, et al. Deep Learning for Hyperspectral Image Classification: An Overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6690-6709.
[2] [2] TAN Kun, MA Weibo, WU Fuyu, et al. Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data[J]. Environmental Monitoring & Assessment, 2019, 191(7): 1-14.
TAN Kun, MA Weibo, WU Fuyu, et al. Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data[J]. Environmental Monitoring & Assessment, 2019, 191(7): 1-14.
[3] [3] CAO Xianghai, WANG Xiaozhen, WANG Da, et al. Densely connected deep random forest for hyperspectral imagery classification[J]. International Journal of Remote Sensing, 2018, 40(3): 1-16.
CAO Xianghai, WANG Xiaozhen, WANG Da, et al. Densely connected deep random forest for hyperspectral imagery classification[J]. International Journal of Remote Sensing, 2018, 40(3): 1-16.
[4] [4] ZHANG Cuifen, YANG Xiaoxia, HAO Lina, et al. Lithological classification by synergizing hyperspectral Hyperion and high resolution WorldView-2 satellite images[J]. Journal of Chengdu University of Technology: Science & Technology Edition, 2017, 44(5): 613-622.
ZHANG Cuifen, YANG Xiaoxia, HAO Lina, et al. Lithological classification by synergizing hyperspectral Hyperion and high resolution WorldView-2 satellite images[J]. Journal of Chengdu University of Technology: Science & Technology Edition, 2017, 44(5): 613-622.
[7] [7] LIU Q, CAI Y, JIANG H, et al. Traffic state prediction using ISOMAP manifold learning[J]. Physica A: Statistical Mechanics and its Applications, 2018, 506(15): 532-541.
LIU Q, CAI Y, JIANG H, et al. Traffic state prediction using ISOMAP manifold learning[J]. Physica A: Statistical Mechanics and its Applications, 2018, 506(15): 532-541.
[9] [9] LI S, WANG Z, LI Y. Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed Range-Free Localization of Wireless Sensor Networks[J]. Neural Processing Letters, 2013, 37(3): 411-424.
LI S, WANG Z, LI Y. Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed Range-Free Localization of Wireless Sensor Networks[J]. Neural Processing Letters, 2013, 37(3): 411-424.
[10] [10] A Krizhevsky, I Sutskever, G E Hinton. Image Net classification with deep convolutional neural networks[C]//Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2012, 25(2): 1097-1105.
A Krizhevsky, I Sutskever, G E Hinton. Image Net classification with deep convolutional neural networks[C]//Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2012, 25(2): 1097-1105.
[13] [13] ZHANG L, ZHANG L, DU B. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geosci. Remote Sens. Mag., 2016, 4(2): 22-40.
ZHANG L, ZHANG L, DU B. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geosci. Remote Sens. Mag., 2016, 4(2): 22-40.
[14] [14] CHENG G, ZHOU P, HAN J. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Trans. Geosci. Remote Sens., 2016, 54(12): 7405-7415.
CHENG G, ZHOU P, HAN J. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Trans. Geosci. Remote Sens., 2016, 54(12): 7405-7415.
[15] [15] YAO X, HAN J, CHENG G, et al. Semantic annotation of high-resolution satellite images via weakly supervised learning[J]. IEEE Trans. Geosci. Remote Sens., 2015, 4(6): 3660-3671.
YAO X, HAN J, CHENG G, et al. Semantic annotation of high-resolution satellite images via weakly supervised learning[J]. IEEE Trans. Geosci. Remote Sens., 2015, 4(6): 3660-3671.
[16] [16] CHEN Y, LIN Z, ZHAO X, et al. Deep learning-based classification of hyperspectral data[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7(6): 2094-2107.
CHEN Y, LIN Z, ZHAO X, et al. Deep learning-based classification of hyperspectral data[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7(6): 2094-2107.
[17] [17] LIU Y, CAO G, SUN Q, et al. Hyperspectral classification via deep networks and superpixel segmentation[J]. Int. J. Remote Sens., 2015, 36(13): 3459-3482.
LIU Y, CAO G, SUN Q, et al. Hyperspectral classification via deep networks and superpixel segmentation[J]. Int. J. Remote Sens., 2015, 36(13): 3459-3482.
[18] [18] MA X, WANG H, GENG J. Spectral-spatial classification of hyperspectral image based on deep auto-encoder[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2016, 9(9): 4073-4085.
MA X, WANG H, GENG J. Spectral-spatial classification of hyperspectral image based on deep auto-encoder[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2016, 9(9): 4073-4085.
[19] [19] CHEN Y, ZHAO X, JIA X. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2015, 8(6): 2381-2392.
CHEN Y, ZHAO X, JIA X. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2015, 8(6): 2381-2392.
[20] [20] ZHONG P, GONG Z, LI S, et al. Learning to diversify deep belief networks for hyperspectral image classification[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(6): 3516-3530.
ZHONG P, GONG Z, LI S, et al. Learning to diversify deep belief networks for hyperspectral image classification[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(6): 3516-3530.
[22] [22] CHEN Y, JIANG H, LI C, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Trans. Geosci. Remote Sens., 2016, 54(10): 6232-6251.
CHEN Y, JIANG H, LI C, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Trans. Geosci. Remote Sens., 2016, 54(10): 6232-6251.
[23] [23] LI W, WU G, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(2): 844-853.
LI W, WU G, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(2): 844-853.
[25] [25] Enayattabar M, Ebrahimnejad A, Motameni H. Dijkstra algorithm for shortest path problem under interval-valued Pythagorean fuzzy environment[J]. Complex & Intelligent Systems, 2019, 5(2): 93-100.
Enayattabar M, Ebrahimnejad A, Motameni H. Dijkstra algorithm for shortest path problem under interval-valued Pythagorean fuzzy environment[J]. Complex & Intelligent Systems, 2019, 5(2): 93-100.
[28] [28] HU W, HUANG Y, LI W, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015(2): 1-12.
HU W, HUANG Y, LI W, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015(2): 1-12.
[29] [29] K Makantasis, K Karantzalos, A Doulamis, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]//IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015: 4959-4962.
K Makantasis, K Karantzalos, A Doulamis, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]//IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015: 4959-4962.
[30] [30] LI W, WU G, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(2): 844-853.
LI W, WU G, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Trans. Geosci. Remote Sens., 2017, 55(2): 844-853.
[31] [31] LEE H, KWON H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Trans. Image Process., 2017, 26(10): 4843-4855.
LEE H, KWON H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Trans. Image Process., 2017, 26(10): 4843-4855.
[32] [32] ZHONG M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2018, 27(6): 2623-2634.
ZHONG M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2018, 27(6): 2623-2634.
Get Citation
Copy Citation Text
QI Yongfeng, CHEN Jing, HUO Yuanlian, LI Fayong. Hyperspectral Image Classification Algorithm Based on Multiscale Convolutional Neural Network[J]. Infrared Technology, 2020, 42(9): 855
Category:
Received: Nov. 28, 2019
Accepted: --
Published Online: Oct. 27, 2020
The Author Email: Yongfeng QI (qiyf@nwnu.edu.cn)
CSTR:32186.14.