Hyperspectral image classification based on deep deterministic policy gradient

Jian Zhou; Qianqian Cheng; Yu Su; Yuhe Qiu; Hu He; Jiawei Huang; Shuijie Wang

doi:10.1117/12.2668126

23 February 2023 Hyperspectral image classification based on deep deterministic policy gradient

Jian Zhou, Qianqian Cheng, Yu Su, Yuhe Qiu, Hu He, Jiawei Huang, Shuijie Wang

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 1255112 (2023) https://doi.org/10.1117/12.2668126
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

Hyperspectral images contain large amounts of information and have high spectral resolution. The performance of hyperspectral images in describing and distinguishing target categories has been greatly improved. With the development of unmanned aerial vehicles (UAVs), a lightweight, adaptable and low-cost way has greatly expanded the application field of hyperspectral images. This paper proposes a spatial spectrum attention mechanism based on Deep Deterministic Policy Gradient (DDPG) for hyperspectral classification. This attention mechanism is combined with 3DCNN to assign different weights to different channels in the classification process. The classification accuracy is improved by activating the useful spatial spectrum information and suppressing the useless spatial spectrum information in the hyperspectral image. A large number of experiments have been carried out to prove the effectiveness of the structure.

Citation Download Citation

Jian Zhou, Qianqian Cheng, Yu Su, Yuhe Qiu, Hu He, Jiawei Huang, and Shuijie Wang "Hyperspectral image classification based on deep deterministic policy gradient", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 1255112 (23 February 2023); https://doi.org/10.1117/12.2668126

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KEYWORDS

Hyperspectral imaging

Image classification

3D modeling

Image processing

Machine learning

Remote sensing

Unmanned aerial vehicles

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