Hyperspectral image classification using Walsh Hadamard transform- based key band selection and deep convolutional neural networks

References

• Ahmad, M., A. M. Khan, M. Mazzara, S. Distefano, M. Ali, and M. S. Sarfraz. 2020. “A Fast and Compact 3-D CNN for Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2020.3043710.
   Google Scholar
• Ari, A. 2023. “Multipath Feature Fusion for Hyperspectral Image Classification Based on Hybrid 3D/2D CNN and Squeeze-Excitation Network.” Earth Science Informatics 16 (1): 175–191.
   Web of Science ®Google Scholar
• Ben Hamida, A., A. Benoit, P. Lambert, and C. Ben Amar. 2018. “3-D Deep Learning Approach for Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 56 (8): 4420–4434. https://doi.org/10.1109/TGRS.2018.2818945.
   Web of Science ®Google Scholar
• Cover, T., and P. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory / Professional Technical Group on Information Theory 13 (1): 21–27. https://doi.org/10.1109/TIT.1967.1053964.
   Web of Science ®Google Scholar
• Feng, F., S. Wang, C. Wang, and J. Zhang. 2019. “Learning Deep Hierarchical Spatial–Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN.” Sensors 19 (23): 5276. https://doi.org/10.3390/s19235276.
   PubMed Web of Science ®Google Scholar
• Firat, H., M. E. Asker, M. İ. Bayindir, and D. Hanbay. 2023. “3D Residual Spatial–Spectral Convolution Network for Hyperspectral Remote Sensing Image Classification.” Neural Computing & Applications 35 (6): 4479–4497. https://doi.org/10.1007/s00521-022-07933-8.
   Web of Science ®Google Scholar
• Fu, H., A. Zhang, G. Sun, J. Ren, X. Jia, Z. Pan, and H. Ma. 2022. “A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 60:1–3. https://doi.org/10.1109/TGRS.2022.3189015.
   Web of Science ®Google Scholar
• Ghaderizadeh, S., D. Abbasi-Moghadam, A. Sharifi, A. Tariq, and S. Qin. 2022. “Multiscale Dual-Branch Residual Spectral–Spatial Network with Attention for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:5455–5467. https://doi.org/10.1109/JSTARS.2022.3188732.
   Web of Science ®Google Scholar
• Hamida, A. B., A. Benoit, P. Lambert, and C. B. Amar. 2018. “3-D Deep Learning Approach for Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 56 (8): 4420–4434. https://doi.org/10.1109/TGRS.2018.2818945.
   Web of Science ®Google Scholar
• Hu, X., X. Wang, Y. Zhong, and L. Zhang. 2022. “S3ANet: Spectral-Spatial-Scale Attention Network for End-To-End Precise Crop Classification Based on UAV-Borne H2 Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 183:147–63. https://doi.org/10.1016/j.isprsjprs.2021.10.014.
   Web of Science ®Google Scholar
• Hu, X., Y. Zhong, X. Wang, C. Luo, J. Zhao, L. Lei, and L. Zhang. 2021. “SPNet: Spectral Patching End-To-End Classification Network for UAV-Borne Hyperspectral Imagery with High Spatial and Spectral Resolutions.” IEEE Transactions on Geoscience & Remote Sensing 60:1–7. https://doi.org/10.1109/TGRS.2021.3049292.
   Google Scholar
• Iyer, P., A. Sriram, and S. Lal. 2021. “Deep Learning Ensemble Method for Classification of Satellite Hyperspectral Images.” Remote Sensing Applications: Society & Environment 23:100580. https://doi.org/10.1016/j.rsase.2021.100580.
   Google Scholar
• Lakshmi Priya GG., Domnic, S. 2014. “Walsh–Hadamard Transform Kernel-Based Feature Vector for Shot Boundary Detection.” IEEE Transactions on Image Processing 23 (12): 5187–97. https://doi.org/10.1109/TIP.2014.2362652.
   PubMed Web of Science ®Google Scholar
• Li, L., M. A. Hassan, S. Yang, F. Jing, M. Yang, A. Rasheed, J. Wang, X. Xia, Z. He, and Y. Xiao. 2022. “Development of Image-Based Wheat Spike Counter Through a Faster R-CNN Algorithm and Application for Genetic Studies.” The Crop Journal 10 (5): 1303–11. https://doi.org/10.1016/j.cj.2022.07.007.
   Google Scholar
• Li, Y., H. Zhang, and Q. Shen. 2017. “Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network, Remote Sensing.” Remote Sensing 9 (1): 67. https://doi.org/10.3390/rs9010067.
   Web of Science ®Google Scholar
• Li, R., S. Zheng, C. Duan, Y. Yang, and X. Wang. 2020. “Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network.” Remote Sensing 12 (3): 582. https://doi.org/10.3390/rs12030582.
   Web of Science ®Google Scholar
• Makantasis, K., K. Karantzalos, A. Doulamis, and N. Doulamis. 2015. “Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks.” International Geoscience Remote Sensor Symposium 2015:4959–4962.
   Google Scholar
• Niu, B., Q. Feng, B. Chen, C. Ou, Y. Liu, and J. Yang. 2022. “HSI-TransUNet: A Transformer Based Semantic Segmentation Model for Crop Mapping from UAV Hyperspectral Imagery.” Computers and Electronics in Agriculture 201:107297. https://doi.org/10.1016/j.compag.2022.107297.
   Web of Science ®Google Scholar
• Pratt, W., J. Kane, and H. C. Andrews. 1969. “Hadamard transform image coding.” Proceedings of the IEEE 57 (1): 58–68. https://doi.org/10.1109/PROC.1969.6869.
   Web of Science ®Google Scholar
• Roy, S. K., G. Krishna, S. R. Dubey, and B. B. Chaudhuri. 2019. “HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification.” IEEE Geoscience & Remote Sensing Letters 17 (2): 277–281. https://doi.org/10.1109/LGRS.2019.2918719.
   Web of Science ®Google Scholar
• Steinwart, I., and A. Christmann. 2008. Support Vector Machines. Berlin, Germany: Springer Verlag.
   Google Scholar
• Sun, W., M. Jiang, W. Li, and Y. Liu. 2016. “A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification.” Remote Sensing 8 (3): 238. https://doi.org/10.3390/rs8030238.
   Web of Science ®Google Scholar
• Wang, Q., F. Zhang, and X. Li. 2018 May 9. “Optimal Clustering Framework for Hyperspectral Band Selection”. IEEE Transactions on Geoscience and Remote Sensing 56 (10): 5910–5922.
   Web of Science ®Google Scholar
• Xie, F., C. Lei, J. Yang, and C. Jin. 2019. “An Effective Classification Scheme for Hyperspectral Image Based on Superpixel and Discontinuity Preserving Relaxation.” Remote Sensing 11 (10): 1149. https://doi.org/10.3390/rs11101149.
   Web of Science ®Google Scholar
• Xu, B., X. Li, W. Hou, Y. Wang, and Y. Wei. 2021. “A Similarity-Based Ranking Method for Hyperspectral Band Selection.” IEEE Transactions on Geoscience and Remote Sensing 59 (11): 9585–99. https://doi.org/10.1109/TGRS.2020.3048138.
   Web of Science ®Google Scholar
• Zhong, Y., X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang. 2020. “WHU-Hi: UAV-Borne Hyperspectral with High Spatial Resolution (H2) Benchmark Datasets and Classifier for Precise Crop Identification Based on Deep Convolutional Neural Network with CRF.” Remote Sensing of Environment 250:112012. https://doi.org/10.1016/j.rse.2020.112012.
   Web of Science ®Google Scholar