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Semi-supervised deep learning for hyperspectral image classification.
- Source :
- Remote Sensing Letters; Apr2019, Vol. 10 Issue 4, p353-362, 10p
- Publication Year :
- 2019
-
Abstract
- Recently, a series of deep learning methods based on the convolutional neural networks (CNNs) have been introduced for classification of hyperspectral images (HSIs). However, in order to obtain the optimal parameters, a large number of training samples are required in the CNNs to avoid the overfitting problem. In this paper, a novel method is proposed to extend the training set for deep learning based hyperspectral image classification. First, given a small-sample-size training set, the principal component analysis based edge-preserving features (PCA-EPFs) and extended morphological attribute profiles (EMAPs) are used for HSI classification so as to generate classification probability maps. Second, a large number of pseudo training samples are obtained by the designed decision function which depends on the classification probabilities. Finally, a deep feature fusion network (DFFN) is applied to classify HSI with the training set consists of the original small-sample-size training set and the added pseudo training samples. Experiments performed on several hyperspectral data sets demonstrate the state-of-the-art performance of the proposed method in terms of classification accuracies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2150704X
- Volume :
- 10
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Remote Sensing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 134455921
- Full Text :
- https://doi.org/10.1080/2150704X.2018.1557787