Hyperspectral Image Classification using PCA Dimensionality Reduction and 3D CNN Deep learning
Keywords:
Hyperspectral image classification, 3D CNN, PCA, deep learning, spectral spatial integration, remote sensingAbstract
Hyperspectral image classification is a crucial task in remote sensing, enabling the detailed analysis and identification of materials on the Earth's surface. The primary objective of this research is to address the limitations of existing classification methods by introducing a novel deep learning-based approach that efficiently handles the high dimensionality and complexity of hyperspectral data.
References
Gao, Kuiliang, Bing Liu, Xuchu Yu, Jinchun Qin, Pengqiang Zhang, and Xiong Tan. "Deep relation network for hyperspectral image few-shot classification." Remote Sensing 12, no. 6 (2020): 923.
Zhu, Minghao, Licheng Jiao, Fang Liu, Shuyuan Yang, and Jianing Wang. "Residual spectral–spatial attention network for hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing 59, no. 1 (2020): 449-462.
Qing, Yuhao, Wenyi Liu, Liuyan Feng, and Wanjia Gao. "Improved transformer net for hyperspectral image classification." Remote Sensing 13, no. 11 (2021): 2216.
