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Proceedings Paper

Transfer learning for hyperspectral image classification using convolutional neural network
Author(s): Yao Liu; Chenchao Xiao
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Paper Abstract

Deep-learning (DL) based classification methods have been successfully used for hyperspectral image classification in recent years. Among various DL-based methods, convolutional neural network (CNN) has attracted a lot of attention. However, limited number of samples restricts the DL-based methods for widespread application. To deal with this problem, we propose a classification framework that can be transfer-learned between hyperspectral data with different number of bands. First, band selection is conducted to retain same number of bands for imagery of different hyperspectral sensors. Second, we simplify the typical 1D-CNN architecture by removing max-pooling layers. Third, modified CNN is trained on a source data, and this pretrained CNN is then fine-tuned with the target data. In the experiment, we pretrain the proposed network using the Indian Pines scene, and then fine-tune parameters to classify pixels in the Botswana scene. According to classification results, this proposed method obtains the highest overall accuracy, compared to KNN, SVM and its corresponding original 1D-CNN model, and even spend the least time training. Therefore, it can be concluded that this proposed method indicate transfer learning can be used between different hyperspectral images, and be helpful to improve the classification efficiency.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320E (14 February 2020); doi: 10.1117/12.2538564
Show Author Affiliations
Yao Liu, Ministry of Natural Resources of the People's Republic of China (China)
Chenchao Xiao, Ministry of Natural Resources of the People's Republic of China (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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