Citation: | GAO Kuiliang, YU Xuchu, ZHANG Pengqiang, TAN Xiong, LIU Bing. Hyperspectral Image Spatial-Spectral Classification Using Capsule Network Based Method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3): 428-437. DOI: 10.13203/j.whugis20200008 |
[1] |
Chen Y S, Jiang H L, Li C Y, et al. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251 doi: 10.1109/TGRS.2016.2584107
|
[2] |
Chen Y S, Lin Z H, Zhao X, et al. Deep LearningBased Classification of Hyperspectral Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107 doi: 10.1109/JSTARS.2014.2329330
|
[3] |
Tao C, Pan H B, Li Y S, et al. Unsupervised Spectral-Spatial Feature Learning with Stacked Sparse Autoencoder for Hyperspectral Imagery Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2438-2442 doi: 10.1109/LGRS.2015.2482520
|
[4] |
Li T, Zhang J P, Zhang Y. Classification of Hyperspectral Image Based on Deep Belief Networks[C]// IEEE International Conference on Image Processing, Paris, France, 2014
|
[5] |
Chen Y S, Zhao X, Jia X P. Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2381-2392 doi: 10.1109/JSTARS.2015.2388577
|
[6] |
Zhang X R, Sun Y J, Jiang K, et al. Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(11): 4141-4155 doi: 10.1109/JSTARS.2018.2844873
|
[7] |
Liu Bing, Yu Xuchu, Yu Anzhu, et al. Deep Convolutional Recurrent Neural Network with Transfer Learning for Hyperspectral Image Classification[J]. Journal of Applied Remote Sensing, 2018, 12(2): 026028
|
[8] |
Hu W, Huang Y Y, Wei L, et al. Deep Convolutional Neural Networks for Hyperspectral Image Classification[J]. Journal of Sensors, 2015, 2015: 1-12
|
[9] |
Mei S H, Ji J Y, Bi Q Q, et al. Integrating Spectral and Spatial Information into Deep Convolutional Neural Networks for Hyperspectral Classification [C]//IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016
|
[10] |
Li W, Wu G D, Zhang F, et al. Hyperspectral Image Classification Using Deep Pixel-Pair Features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853 doi: 10.1109/TGRS.2016.2616355
|
[11] |
Yue J, Mao S J, Li M. A Deep Learning Framework for Hyperspectral Image Classification Using Spatial Pyramid Pooling[J]. Remote Sensing Letters, 2016, 7(9): 875-884 doi: 10.1080/2150704X.2016.1193793
|
[12] |
Zhang M M, Li W, Du Q. Diverse Region-Based CNN for Hyperspectral Image Classification[J]. IEEE Transactions on Image Processing, 2018, 27 (6): 2623-2634 doi: 10.1109/TIP.2018.2809606
|
[13] |
职露, 余旭初, 邹滨, 等. 多层级二值模式的高光谱影像空-谱分类[J]. 武汉大学学报∙信息科学版, 2019, 44(11): 1659-1666 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201911010.htm
Zhi Lu, Yu Xuchu, Zou Bin, et al. A Multi-Layer Binary Pattern Based Method for Hyperspectral Imagery Classification Using Combined Spatial-Spectral Characteristics[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1659-1666 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201911010.htm
|
[14] |
李竺强, 朱瑞飞, 高放, 等. 三维卷积神经网络模型联合条件随机场优化的高光谱遥感影像分类[J]. 光学学报, 2018, 38(8): 404-413 https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201808046.htm
Li Zhuqiang, Zhu Ruifei, Gao Fang, et al. Hyperspectral Remote Sensing Image Classification Based on Three-Dimensional Convolution Neural Network Combined with Conditional Random Field Optimization[J]. Acta Optica Sinica, 2018, 38(8): 404-413 https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201808046.htm
|
[15] |
Gao K L, Liu B, Yu X C, et al. Deep Relation Network for Hyperspectral Image Few-Shot Classification[J]. Remote Sensing, 2020, 12(6): 923 doi: 10.3390/rs12060923
|
[16] |
刘冰, 余旭初, 张鹏强, 等. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1): 53-63 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201901008.htm
Liu Bing, Yu Xuchu, Zhang Pengqiang, et al. Deep 3D Convolutional Network Combined with SpatialSpectral Features for Hyperspectral Image Classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 53-63 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201901008.htm
|
[17] |
Yang J, Zhao Y, Chan J C. Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8): 4729-4742 doi: 10.1109/TGRS.2017.2698503
|
[18] |
Haut J M, Paoletti M E, Plaza J, et al. Active Learning with Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 64406461 doi: 10.1109/TGRS.2018.2838665
|
[19] |
Wang L G, Hao S Y, Wang Q M, et al. Semi-supervised Classification for Hyperspectral Imagery Based on Spatial-Spectral Label Propagation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 97: 123-137 doi: 10.1016/j.isprsjprs.2014.08.016
|
[20] |
Patrick M K, Adekoya A F, Mighty A A, et al. Capsule Networks: A Survey[J]. Journal of King Saud University-Computer and Information Sciences, 2019
|
[21] |
Liu B, Yu X C, Zhang P Q, et al. Supervised Deep Feature Extraction for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 1909-1921 doi: 10.1109/TGRS.2017.2769673
|
[22] |
Lee H, Kwon H. Going Deeper with Contextual CNN for Hyperspectral Image Classification[J]. IEEE Transactions on Image Processing, 2017, 26 (10): 4843-4855 doi: 10.1109/TIP.2017.2725580
|
[23] |
Gu J X, Wang Z H, Kuen J, et al. Recent Advances in Convolutional Neural Networks[J]. Pattern Recognition, 2018, 77: 354-377 doi: 10.1016/j.patcog.2017.10.013
|
[24] |
Sabour S, Frosst N, Hinton G E. Dynamic Routing Between Capsules[C]//Conference and Workshop on Neural Information Processing Systems, Long Beach, CA, USA, 2017
|
[25] |
Glorot X, Bengio Y. Understanding the Difficulty of Training Deep Feedforward Neural Networks[C]// International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy, 2010
|
[26] |
Benediktsson J A, Palmason J A, Sveinsson J R. Classification of Hyperspectral Data from Urban Areas Based on Extended Morphological Profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 480-491 doi: 10.1109/TGRS.2004.842478
|
[1] | MENG Yiyue, GUO Chi, LIU Jingnan. Deep Reinforcement Learning Visual Target Navigation Method Based on Attention Mechanism and Reward Shaping[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1100-1108. DOI: 10.13203/j.whugis20230193 |
[2] | GAO Kuiliang, LIU Bing, YU Xuchu, YU Anzhu, SUN Yifan. Automatic Network Structure Search Method for Hyperspectral Image Classification[J]. Geomatics and Information Science of Wuhan University, 2024, 49(2): 225-235. DOI: 10.13203/j.whugis20210380 |
[3] | WANG Jie, LIU Jiahang, LING Xinpeng, DUAN Zexian. Deep Learning-Based Joint Local and Non-local InSAR Image Phase Filtering Method[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240052 |
[4] | GUO Congzhou, LI Ke, LI He, TONG Xiaochong, WANG Xiwen. Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292 |
[5] | LI Yansheng, ZHANG Yongjun. A New Paradigm of Remote Sensing Image Interpretation by Coupling Knowledge Graph and Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1176-1190. DOI: 10.13203/j.whugis20210652 |
[6] | JI Shunping, LUO Chong, LIU Jin. A Review of Dense Stereo Image Matching Methods Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2021, 46(2): 193-202. DOI: 10.13203/j.whugis20200620 |
[7] | ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650 |
[8] | JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132 |
[9] | PAN Yin, SHAO Zhenfeng, CHENG Tao, HE Wei. Analysis of Urban Waterlogging Influence Based on Deep Learning Model[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 132-138. DOI: 10.13203/j.whugis20170217 |
[10] | FAN Heng, XU Jun, DENG Yong, XIANG Jinhai. Behavior Recognition of Human Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 492-497. DOI: 10.13203/j.whugis20140110 |