  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.
