A novel hyperspectral image classification iteration method based on deep learning

Qian Liu; Peiyang Jin; Botao Zhu; Keming Mao

doi:10.1117/12.2611671

22 October 2021 A novel hyperspectral image classification iteration method based on deep learning

Qian Liu, Peiyang Jin, Botao Zhu, Keming Mao

Proceedings Volume 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021); 119280R (2021) https://doi.org/10.1117/12.2611671
Event: International Conference on Image Processing and Intelligent Control (IPIC 2021), 2021, Lanzhou, China

Abstract

Hyperspectral image(HSI) classification is a research hotspot for its wide application. However, to obtain labeled HSI image data is time consuming and with high cost, which makes it difficult to design an optimal classifier. In this paper, we propose a novel HSI classification method based on active learning. Before the iteration begins, training patch set is used to train a CNN model. Based on the classification result of current model, a carefully-designed patch selection strategy is employed to select patches. In each iteration, we fine-tune the current CNN model with the selected HSI patches. Repeat the iteration until the final classification accuracy is satisfactory. Comprehensive experiments on the University of Pavia, Indian Pines, and Salinas demonstrate the effectiveness of the proposed method.

Citation Download Citation

Qian Liu, Peiyang Jin, Botao Zhu, and Keming Mao "A novel hyperspectral image classification iteration method based on deep learning", Proc. SPIE 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021), 119280R (22 October 2021); https://doi.org/10.1117/12.2611671

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