Active deep densely connected convolutional network for hyperspectral image classification

References

• Anand, R., S. Veni, and J. Aravinth. 2021. “Robust Classification Technique for Hyperspectral Images Based on 3d-discrete Wavelet Transform.” Remote Sensing 13 (7).
   PubMed Web of Science ®Google Scholar
• Bazi, Y., and F. Melgani. 2010. “Gaussian Process Approach to Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 48 (1): 186–197.
   Web of Science ®Google Scholar
• Bazi, Y., N. Alajlan, F. Melgani, H. AlHichri, S. Malek, and R. R. Yager. 2014. “Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 11 (6): 1066–1070.
   Web of Science ®Google Scholar
• Cao, X., J. Yao, Z. Xu, and D. Meng. 2020. “Hyperspectral Image Classification with Convolutional Neural Network and Active Learning.” IEEE Transactions on Geoscience and Remote Sensing58 (7): 1–13. doi: 10.1109/TGRS.2020.2964627
   Google Scholar
• Chen, Y., X. Zhao, and X. Jia. 2015. “Spectral–spatial Classification of Hyperspectral Data Based on Deep Belief Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 2381–2392.
   Web of Science ®Google Scholar
• Chen, Y., Z. Lin, X. Zhao, G. Wang, and Y. Gu. 2014. “Deep Learning-based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107.
   Web of Science ®Google Scholar
• Dalla Mura, M., J. A. Benediktsson, and L. Bruzzone (2010). “Classification of Hyperspectral Images with Extended Attribute Profiles and Feature Extraction Techniques.” In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, Honolulu, Hawaii, U.S.A, pp. 76–79. IEEE.
   Google Scholar
• Danka, T., and P. Horvath. 2018. modAL: A Modular Active Learning Framework for Python. arXiv: Learning.
   Google Scholar
• Deng, C., Y. Xue, X. Liu, C. Li, and D. Tao. 2019. “Active Transfer Learning Network: A Unified Deep Joint Spectral–spatial Feature Learning Model for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (3): 1741–1754.
   Web of Science ®Google Scholar
• Di, W., and M. M. Crawford (2010). “Multi-view Adaptive Disagreement Based Active Learning for Hyperspectral Image Classification.” pp. 1374–1377.
   Google Scholar
• Ghamisi, P., M. Dalla Mura, and J. A. Benediktsson. 2015. “A Survey on Spectral–spatial Classification Techniques Based on Attribute Profiles.” IEEE Transactions on Geoscience and Remote Sensing 53 (5): 2335–2353.
   Web of Science ®Google Scholar
• Guo, H., J. Liu, J. Yang, Z. Xiao, and Z. Wu. 2020. “Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-d Cnn and 3-d Cnn.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 4789–4802.
   Web of Science ®Google Scholar
• Hang, R., Q. Liu, D. Hong, and P. Ghamisi. 2019. “Cascaded Recurrent Neural Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 5384–5394.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun (2016). “Deep Residual Learning for Image Recognition.” pp. 770–778.
   Google Scholar
• Huang, G., Z. Liu, L. V. Der Maaten, and K. Q. Weinberger (2017). “Densely Connected Convolutional Networks.” pp. 2261–2269.
   Google Scholar
• Ioffe, S., and C. Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv: Learning.
   Google Scholar
• Jia, S., J. Hu, J. Zhu, X. Jia, and Q. Li. 2017. “Three-dimensional Local Binary Patterns for Hyperspectral Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (4): 2399–2413.
   Web of Science ®Google Scholar
• Jia, S., L. Shen, and Q. Li. 2015. “Gabor Feature-based Collaborative Representation for Hyperspectral Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 1118–1129.
   Web of Science ®Google Scholar
• Kingma, D. P., and J. Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv: Learning.
   Google Scholar
• Li, C., Y. Ma, X. Mei, C. Liu, and J. Ma. 2016. “Hyperspectral Image Classification with Robust Sparse Representation.” IEEE Geoscience and Remote Sensing Letters 13 (5): 641–645.
   Web of Science ®Google Scholar
• Li, J. (2016). “Active Learning for Hyperspectral Image Classification with a Stacked Autoencoders Based Neural Network.” pp. 1062–1065.
   Google Scholar
• Li, W., C. Chen, H. Su, and Q. Du. 2015. “Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 3681–3693.
   Web of Science ®Google Scholar
• Li, W., G. Wu, F. Zhang, and Q. Du. 2017. “Hyperspectral Image Classification Using Deep Pixel-pair Features.” IEEE Transactions on Geoscience and Remote Sensing 55 (2): 844–853.
   Web of Science ®Google Scholar
• Li, W., and Q. Du. 2014. “Joint Within-class Collaborative Representation for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2200–2208.
   Web of Science ®Google Scholar
• Li, Z., T. Wang, W. Li, Q. Du, C. Wang, C. Liu, and X. Shi. 2020. “Deep Multilayer Fusion Dense Network for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 1258–1270.
   Web of Science ®Google Scholar
• Lin, J., L. Zhao, S. Li, R. Ward, and Z. J. Wang. 2018. “Active-learning-incorporated Deep Transfer Learning for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (11): 4048–4062.
   Web of Science ®Google Scholar
• Liu, B., X. Yu, P. Zhang, X. Tan, R. Wang, and L. Zhi. 2018. “Spectral–spatial Classification of Hyperspectral Image Using Three-dimensional Convolution Network.” Journal of Applied Remote Sensing 12 (1): 016005. doi:10.1117/1.JRS.12.042608
   Web of Science ®Google Scholar
• Liu, B., X. Yu, A. Yu, P. Zhang, and G. Wan. 2018. “Spectral-spatial Classification of Hyperspectral Imagery Based on Recurrent Neural Networks.” Remote Sensing Letters 9 (12): 1118–1127.
   Web of Science ®Google Scholar
• Liu, B., X. Yu, P. Zhang, A. Yu, Q. Fu, and X. Wei. 2018. “Supervised Deep Feature Extraction for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (4): 1909–1921.
   Web of Science ®Google Scholar
• Liu, B., X. Yu, A. Yu, P. Zhang, G. Wan, and R. Wang. 2019. “Deep Few-shot Learning for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (4): 2290–2304.
   Web of Science ®Google Scholar
• Liu, B., A. Yu, X. Yu, R. Wang, K. Gao, and W. Guo. 2020. “Deep Multiview Learning for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 1–15.
   Web of Science ®Google Scholar
• Liu, B., W. Guo, X. Chen, K. Gao, X. Zuo, R. Wang, and A. Yu. 2020. “Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image.” IEEE Access 1.
   Web of Science ®Google Scholar
• Liu, B., K. Gao, A. Yu, W. Guo, R. Wang, and X. Zuo. 2020. “Semisupervised Graph Convolutional Network for Hyperspectral Image Classification.” Journal of Applied Remote Sensing 14 (2): 1–14.
   Web of Science ®Google Scholar
• Liu, B., X. Yu, P. Zhang, X. Tan, A. Yu, and Z. Xue. 2017. “A Semi-supervised Convolutional Neural Network for Hyperspectral Image Classification.” Remote Sensing Letters 8 (9): 839–848.
   Web of Science ®Google Scholar
• Liu, J., Z. Wu, L. Xiao, J. Sun, and H. Yan. 2020. “A Truncated Matrix Decomposition for Hyperspectral Image Super-resolution.” IEEE Transactions on Image Processing 29 8028–8042.
   Google Scholar
• Liu, P., H. Zhang, and K. B. Eom. 2017. “Active Deep Learning for Classification of Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (2): 712–724.
   Web of Science ®Google Scholar
• Liu, X., R. Wang, Z. Cai, Y. Cai, and X. Yin. 2019. “Deep Multigrained Cascade Forest for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (10): 8169–8183.
   Web of Science ®Google Scholar
• Mei, S., J. Ji, Y. Geng, Z. Zhang, X. Li, and Q. Du. 2019. “Unsupervised Spatial–spectral Feature Learning by 3d Convolutional Autoencoder for Hyperspectral Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (9): 6808–6820.
   Web of Science ®Google Scholar
• Mitra, P., B. U. Shankar, and S. K. Pal. 2004. “Segmentation of Multispectral Remote Sensing Images Using Active Support Vector Machines.” Pattern Recognition Letters 25 (9): 1067–1074.
   Web of Science ®Google Scholar
• Nair, V., and G. E. Hinton (2010). “Rectified Linear Units Improve Restricted Boltzmann Machines.” pp. 807–814.
   Google Scholar
• Rajan, S., J. Ghosh, and M. M. Crawford. 2008. “An Active Learning Approach to Hyperspectral Data Classification.” IEEE Transactions on Geoscience and Remote Sensing 46 (4): 1231–1242.
   Web of Science ®Google Scholar
• Shen, D., J. Liu, Z. Xiao, J. Yang, and L. Xiao. 2020. “A Twice Optimizing Net with Matrix Decomposition for Hyperspectral and Multispectral Image Fusion.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 4095–4110.
   Web of Science ®Google Scholar
• Waske, B., S. Van Der Linden, J. A. Benediktsson, A. Rabe, and P. Hostert. 2010. “Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 48 (7): 2880–2889.
   Web of Science ®Google Scholar
• Xia, J., M. Dalla Mura, J. Chanussot, P. Du, and X. He. 2015. “Random Subspace Ensembles for Hyperspectral Image Classification with Extended Morphological Attribute Profiles.” IEEE Transactions on Geoscience and Remote Sensing 53 (9): 4768–4786.
   Web of Science ®Google Scholar
• Yoo, D., and I. S. Kweon (2019). “Learning Loss for Active Learning.” pp. 93–102.
   Google Scholar
• Yue, J., S. Mao, and M. Li. 2016. “A Deep Learning Framework for Hyperspectral Image Classification Using Spatial Pyramid Pooling.” Remote Sensing Letters 7 (9): 875–884.
   Web of Science ®Google Scholar
• Zhang, C., G. Li, and S. Du. 2019. “Multi-scale Dense Networks for Hyperspectral Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (11): 9201–9222.
   Web of Science ®Google Scholar
• Zhang, H., Y. Li, Y. Jiang, P. Wang, Q. Shen, and C. Shen. 2019. “Hyperspectral Classification Based on Lightweight 3-d-cnn with Transfer Learning.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 5813–5828.
   Web of Science ®Google Scholar
• Zhang, X., Y. Sun, K. Jiang, C. Li, L. Jiao, and H. Zhou. 2018. “Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (11): 4141–4155.
   Web of Science ®Google Scholar
• Zhang, Z., E. Pasolli, and M. M. Crawford. 2020. “An Adaptive Multiview Active Learning Approach for Spectral–spatial Classification of Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 58 (4): 2557–2570.
   Web of Science ®Google Scholar
• Zhong, Z., J. Li, Z. Luo, and M. Chapman. 2018. “Spectral–spatial Residual Network for Hyperspectral Image Classification: A 3-d Deep Learning Framework.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 847–858.
   Web of Science ®Google Scholar
• Zhu, L., Y. Chen, P. Ghamisi, and J. A. Benediktsson. 2018. “Generative Adversarial Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (9): 5046–5063.
   Web of Science ®Google Scholar