Deep feature representation for hyperspectral image classification

Hyperspectral data classification problems have been extensively studied in the past decade. However, well designed features and a robust classifier are still open issues that impact on the performance of an automatic land-cover classification system. In this paper, we propose a deep feature representation method that generates very good features and a classifier for pixel-wise hyperspectral data classification. The proposed method has two main steps: principle components of the hyperspectral image cube is first filtered by three dimensional Gabor wavelets; second, stacked autoencoders are trained on the outputs of the previous step through unsupervised pre-training, finally deep neural network is trained on those stacked autoencoders. Experimental results obtained on real hyperspectral image confirmed the effectiveness of the proposed approach in favors of the high classification accuracy and computation efficiency.