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Proceedings Paper

Semi-supervised classification of hyperspectral imagery based on stacked autoencoders
Author(s): Qiongying Fu; Xuchu Yu; Xiangpo Wei; Zhixiang Xue
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Paper Abstract

Hyperspectral imagery has high spectral resolution, and spectrum of it has always been non-linear. The traditional classification methods cannot get better result when the number of samples is small. Combined with the theory of deep learning, a new semi-supervised method based on stacked autoencoders (SAE) is proposed for hyperspectral imagery classification. Firstly, with stacked autoencoders, a deep network model is constructed. Then, unsupervised pre-training is carried combined SOFTMAX classifier with unlabeled samples. Finally, fine-tuning the network model with small labeled samples, the SAE-based classifier can be got to learn implicit feature of spectrum of hyperspectral imagery and achieve classification of hyperspectral imagery. According to comparative experiments, the results indicate that the proposed method is effective to improve the hyperspectral imagery classification accuracy in case of small samples.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332B (29 August 2016); doi: 10.1117/12.2245011
Show Author Affiliations
Qiongying Fu, Information Engineering Univ. (China)
Xuchu Yu, Information Engineering Univ. (China)
Xiangpo Wei, Information Engineering Univ. (China)
Zhixiang Xue, Information Engineering Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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