Graph neural network-based remote target classification in hyperspectral imaging

References

• Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Strunk. 2012. “Slic Superpixels Compared to State-Of-The-Art Superpixel Methods.” IEEE Transactions Pattern Analysis Machine Intelligent 34 (11): 2274–2282. https://doi.org/10.1109/TPAMI.2012.120.
   PubMed Web of Science ®Google Scholar
• Akbari, D., A. Ashrafi, and R. Attarzadeh. 2022. “A New Method for Object-Based Hyperspectral Image Classification.” Journal of the Indian Society of Remote Sensing 50 (9): 1761–1771. https://doi.org/10.1007/s12524-022-01563-2.
   Web of Science ®Google Scholar
• Atiya, K., D. V. Amol, M. Shankar, and C. H. Patil. 2022. “A Systematic Review on Hyperspectral Imaging Technology with a Machine and Deep Learning Methodology for Agricultural Applications.” Ecological Informatics 69 (1–14): 101678. https://doi.org/10.1016/j.ecoinf.2022.101678.
   Web of Science ®Google Scholar
• Birkeland, R., S. Berg, M. Orlandic, and J. L. Garrett 2022. “On-Board Characterization of Hyperspectral Image Exposure and Cloud Coverage by Compression Ratio.” 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy, 1–5, https://doi.org/10.1109/WHISPERS56178.2022.9955117.
   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 Sensing 58 (7): 4604–4616. https://doi.org/10.1109/TGRS.2020.2964627.
   Web of Science ®Google Scholar
• Chellaswamy, C., T. S. Geetha, B. Ramasubramanian, R. Abirami, B. Archana, and A. Divya Bharathi 2022. “Optimized Convolutional Neural Network Based Multiple Eye Disease Detection and Information Sharing System, 2022.” 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 1105–1113. https://doi.org/10.1109/ICICCS53718.2022.9788334.
   Google Scholar
• Cheng, X., X. He, M. Qiao, P. Li, P. Chang, T. Zhang, X. Guo, J. Wang, Z. Tian, and G. Zhou. 2022. “Multi-View Graph Convolutional Network with Spectral Component Decompose for Remote Sensing Images Classification.” IEEE Transactions on Circuits and Systems for Video Technology 1–1. https://doi.org/10.1109/TCSVT.2022.3227172.
   Google Scholar
• Dataset. 2022, http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.
   Google Scholar
• Ding, Y., X. Zhao, Z. Zhang, W. Cai, and N. Yang. 2021. “Multiscale Graph Sample and Aggregate Network with Context-Aware Learning for Hyperspectral Image Classification.” IEEE Journal Sel Top Applications Earth Observatory Rem Sens 14:4561–4572. https://doi.org/10.1109/JSTARS.2021.3074469.
   Web of Science ®Google Scholar
• Dorđije, B., O. Milica, and A. J. Tor. 2020. “A Reconfigurable Multi-Mode Implementation of Hyperspectral Target Detection Algorithms.” Microprocessors and Microsystems 78 (1–14): 103258. https://doi.org/10.1016/j.micpro.2020.103258.
   Web of Science ®Google Scholar
• Dozat, T. 2015. “Incorporating Nesterov Momentum into Adam”. [Online]. http://cs229.stanford.edu/proj2015/054_report.pdf.
   Google Scholar
• ElMasry, G., and D. W. Sun. 2010. Principles of Hyperspectral Imaging Technology, Hyperspectral Imaging for Food Quality Analysis and Control, 3–43. Academic Press. https://doi.org/10.1016/B978-0-12-374753-2.10001-2.
   Google Scholar
• Ganesh Babu, R., and C. Chellaswamy. 2022. “Different Stages of Disease Detection in Squash Plant Based on Machine Learning.” Journal of Biosciences 47 (1): 1–15. https://doi.org/10.1007/s12038-021-00241-8.
   PubMed Web of Science ®Google Scholar
• Ganesh Babu, R., C. Chellaswamy, M. Surya Bhupal Rao, M. Saravanan, E. Kanchana, and J. Shalini 2020. “Deep Learning Based Pothole Detection and Reporting System.” 2020 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 1–6. https://doi.org/10.1109/ICSSS49621.2020.9202061.
   Google Scholar
• Gaurav, M., G. Himanshu, and S. Prashant Kumar. 2021. “Identification of Malachite and Alteration Minerals Using Airborne AVIRIS-NG Hyperspectral Data.” Quaternary Science Advances 4 (1–13): 100036. https://doi.org/10.1016/j.qsa.2021.100036.
   Google Scholar
• Ghamisi, P., N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, B. Rasti, and A. Plaza. 2017. “Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 37–78. https://doi.org/10.1109/MGRS.2017.2762087.
   Web of Science ®Google Scholar
• Ghulam, F., X. Liang, S. Allah Bux, A. Fazeel, and H. Fazal. 2023. “A Dual Attention Driven Multiscale-Multilevel Feature Fusion Approach for Hyperspectral Image Classification.” International Journal of Remote Sensing 44 (4): 1151–1178. https://doi.org/10.1080/01431161.2023.2176721.
   Web of Science ®Google Scholar
• Haut, J. M., M. E. Paoletti, J. Plaza, A. Plaza, and J. Li. 2019. “Hyperspectral Image Classification Using Random Occlusion Data Augmentation.” IEEE Geoscience & Remote Sensing Letters 16 (11): 1751–1755. https://doi.org/10.1109/LGRS.2019.2909495.
   Web of Science ®Google Scholar
• Kamruzzaman, M., and D. W. Sun. 2016. “Chapter 5 - Introduction to Hyperspectral Imaging Technology.“ In Computer Vision Technology for Food Quality Evaluation, edited by Sun, Da-Wen, 111–139. 2nd ed. Academic Press. https://doi.org/10.1016/B978-0-12-802232-0.00005-0.
   Google Scholar
• Kerekes, J. P., and J. E. Baum. 2003. “Hyperspectral Imaging System Modeling.” Lincoln Laboratory Journal 14 (1): 117–130.
   Google Scholar
• Li, W., Q. Du, F. Zhang, and W. Hu. 2014. “Collaborative-Representation-Based Nearest Neighbor Classifier for Hyperspectral Imagery.” IEEE Geoscience & Remote Sensing Letters 12 (2): 389–393. https://doi.org/10.1109/LGRS.2014.2343956.
   Web of Science ®Google Scholar
• Liu, S., B. Ding, J. Bai, and Z. Xiao 2021. “Hyperspectral Image Classification Based on Extended Morphological Profile Features and Ghost Module.” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 3617–3620, https://doi.org/10.1109/IGARSS47720.2021.9554092.
   Google Scholar
• Lu, G., and S. Bu. 2023. “Adaptive Stability Contingency Screening for Operational Planning Based on Domain-Adversarial Graph Neural Network.” IEEE Transactions on Power Systems 1–14. https://doi.org/10.1109/TPWRS.2023.3262851.
   Web of Science ®Google Scholar
• Martin, S. L., and T. George. 2018. “Applications of Hyperspectral Image Analysis for Precision Agriculture.“ Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 1063916. https://doi.org/10.1117/12.2303921.
   Google Scholar
• Mathworks 2022, https://la.mathworks.com/help/deeplearning/ug/monitor-deep-learning-training-progress.html.
   Google Scholar
• Ma, X., Q. Wang, and X. Tong. 2022. “A Spectral Grouping-Based Deep Learning Model for Haze Removal of Hyperspectral Images.” ISPRS Journal of Photogrammetry and Remote Sensing 188:177–189. https://doi.org/10.1016/j.isprsjprs.2022.04.007.
   Web of Science ®Google Scholar
• Mengyun, D., S. Qi, D. Luanyan, L. Yaohai, W. Lifang, Y. Changcai, and C. Riqing. 2022. “MS3A-Net: Multi-Scale and Spectral-Spatial Attention Network for Hyperspectral Image Classification.” International Journal of Remote Sensing 43 (19–24): 7139–7160. https://doi.org/10.1080/01431161.2022.2155081.
   Web of Science ®Google Scholar
• Monikandan, A. S., C. Chellaswamy, T. S. Geetha, S. S. Sivaraju, and T. Hong. 2022. “Optimized Convolutional Neural Network-Based Capacity Expansion Framework for Electric Vehicle Charging Station.” International Transactions on Electrical Energy Systems 2022:1–21. Article ID 2915910. https://doi.org/10.1155/2022/2915910.
   Web of Science ®Google Scholar
• Panda, A., and D. Pradhan 2015. “Hyperspectral Image Processing for Target Detection Using Spectral Angle Mapping.” In Proceedings of the IEEE International Conference on Industrial Instrumentation and Control (ICIC), 1098–1103. https://doi.org/10.1109/IIC.2015.7150911.
   Google Scholar
• Qin, A., Z. Shang, J. Tian, Y. Wang, T. Zhang, and Y. Tang. 2019. “Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification.” IEEE Geoscience & Remote Sensing Letters 16 (2): 241–245. https://doi.org/10.1109/LGRS.2018.2869563.
   Web of Science ®Google Scholar
• Rodarmel, C., and J. Shan. 2002. “Principal Component Analysis for Hyperspectral Image Classification.” Surveying and Land Information Science 62 (2): 115–122.
   Google Scholar
• Saptalakar, B. K., and M. V. Latte. 2022. “FPGA-Based Reflection Image Removal Using Cognitive Neural Networks.” Applied Nanoscience 13 (3): 1–15. https://doi.org/10.1007/s13204-022-02352-6.
   Web of Science ®Google Scholar
• Shen, Y., S. Zhu, C. Chen, Q. Du, L. Xiao, J. Chen, and D. Pan. 2020. “Efficient Deep Learning of Nonlocal Features for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (7): 6029–6043. https://doi.org/10.1109/TGRS.2020.3014286.
   Web of Science ®Google Scholar
• Tingting, H., S. Weiwei, C. Chao, Y. Gang, M. Xiangchao, and P. Jiangtao. 2022. “Marine Floating Raft Aquaculture Extraction of Hyperspectral Remote Sensing Images-Based Decision Tree Algorithm.” International Journal of Applied Earth Observation and Geoinformation 111 (1–15): 102846. https://doi.org/10.1016/j.jag.2022.102846.
   Web of Science ®Google Scholar
• Tiwari, K. C., M. K. Arora, and D. Singh. 2011. “An Assessment of Independent Component Analysis for Detection of Military Targets from Hyperspectral Images.” International Journal of Applied Earth Observation and Geoinformation 13 (5): 730–740. https://doi.org/10.1016/j.jag.2011.03.007.
   Web of Science ®Google Scholar
• Vo-Dihn, T., D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. Myong Song, R. Jagannathan, and E. Michaud. 2004. “A Hyperspectral Imaging System for in vivo Optical Diagnostics.” IEEE Engineering in Medicine and Biology Magazine 23 (5): 40–49. https://doi.org/10.1109/MEMB.2004.1360407.
   PubMedGoogle Scholar
• Wu, Z., J. Liu, J. Yang, Z. Xiao, and L. Xiao. 2021. “Composite Kernel Learning Network for Hyperspectral Image Classification.” International Journal of Remote Sensing 42 (16): 6066–6089. https://doi.org/10.1080/01431161.2021.1934599.
   Web of Science ®Google Scholar
• Yu, C., J. Huang, M. Song, Y. Wang, and C. I. Chang. 2022. “Edge-Inferring Graph Neural Network with Dynamic Task-Guided Self-Diagnosis for Few-Shot Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2022.3196311.
   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 & Remote Sensing 57 (11): 9201–9222. https://doi.org/10.1109/TGRS.2019.2925615.
   Web of Science ®Google Scholar
• Zhong, S., C.-I. Chang, J. Li, X. Shang, S. Chen, M. Song, Y. Zhang, et al. 2019. “Class Feature Weighted Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (12): 4728–4745. https://doi.org/10.1109/JSTARS.2019.2950876.
   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 (1): 112012. https://doi.org/10.1016/j.rse.2020.112012.
   Web of Science ®Google Scholar
• Zhu, Q., W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, and D. Li. 2021. “A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification.” IEEE Transactions on Cybernetics 1–14. https://doi.org/10.1109/TCYB.2021.3070577.
   Web of Science ®Google Scholar
• Zhu, F., Y. Wang, B. Fan, S. Xiang, G. Meng, and C. Pan. 2014. “Spectral Unmixing via Data-Guided Sparsity.” IEEE Transactions on Image Processing 23 (12): 5412–5427. https://doi.org/10.1109/TIP.2014.2363423.
   PubMed Web of Science ®Google Scholar