Hyperspectral Data Analysis based on Integrated Deep Learning

doi:10.23940/ijpe.20.08.p9.12251234

Abstract

Abstract: Due to the high dimensions of complex hyperspectral data features and the difficulty of feature selection and extraction, it is difficult to construction an inversion model. This paper briefly describes the common methods of hyperspectral data analysis and processing. To address the existing problems in the field of hyperspectral data analysis, an integrated deep learning framework combined with artificial neural network and large GAN is proposed in this paper. The application of this integrated deep learning framework in hyperspectral data analysis is discussed further. Results show that the integrated deep learning method combining neural network and large GAN provides direction for hyperspectral data analysis and processing, which has a broad application prospect.

Key words: hyperspectral data, deep learning, super-resolution reconstruction, generative adversarial nets (GAN), convolutional neural network (CNN)

Zhifeng Zhang, Xiao Cui, Pu Li, Jintao Jiang, and Xiaohui Ji. Hyperspectral Data Analysis based on Integrated Deep Learning [J]. Int J Performability Eng, 2020, 16(8): 1225-1234.

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