A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

• Published in: International journal of applied earth observation and geoinformation 2021-10, Vol.102, p.102459, Article 102459
• Main Authors: Zhao, Jinling, Hu, Lei, Dong, Yingying, Huang, Linsheng, Weng, Shizhuang, Zhang, Dongyan
• Format: Article
• Language: English
• Subjects: 3D convolutional neural network; Deep learning; Hyperspectral remote sensing; Residual network; Spectral-spatial features; Stacked autoencoder
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102459

In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network (3DCNN) was then designed and the residual network module was introduced to build a 3DDRN. The dimension-reduced 3D HSI cubes were input into the 3DDRN to extract identifiable joint spectral-spatial features. Finally, the deep features continuously identified by the 3DDRN were input to Softmax classification layer to realize the classification. In addition, Batch Normalization (BN) and Dropout were used during the learning process to avoid overfitting on training data. The training and test sets of Indian Pines (IP), Pavia University (PU) and Salinas (SA) hyperspectral data sets were selected as the modeling and verification data sources. Six classical classification algorithms were adopted for comparing our proposed method, specifically including conventional machine learning algorithms of Radial Basis Function-Support Vector Machine (RBF-SVM), Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP) and Local Binary Pattern-K-Nearest Neighbor (LBP-KNN), and mainstream deep learning algorithms of Variational Autoencoder (VAE), Convolutional Neural Network (CNN) and Spectral-Spatial Residual Network (SSRN). The results showed that the overall accuracy (OA) reached 98.97%, 99.69% and 99.24%, respectively, only based on 10%, 5% and 1% of training samples for IP, PU and SA. Consequently, the proposed method shows a better classification performance, even in the case of limited samples.