29 August 2016 Semi-supervised classification of hyperspectral imagery based on stacked autoencoders
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100332B (2016) https://doi.org/10.1117/12.2245011
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
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.
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Qiongying Fu, Qiongying Fu, Xuchu Yu, Xuchu Yu, Xiangpo Wei, Xiangpo Wei, Zhixiang Xue, Zhixiang Xue, } "Semi-supervised classification of hyperspectral imagery based on stacked autoencoders", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332B (29 August 2016); doi: 10.1117/12.2245011; https://doi.org/10.1117/12.2245011
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