Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
This addresses a domain-specific problem in remote sensing for researchers and practitioners, with incremental improvements over existing methods.
The paper tackled hyperspectral image classification by introducing a deep learning framework using autoencoders for spectral-spatial feature extraction, achieving the highest classification accuracy and outperforming classical methods like SVM and PCA-based SVM.
Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.