Efficient Deep Auto-Encoder Learning for the Classification of Hyperspectral Images

Hyperspectral Image (HSI) classification is one of the most pervasive issue in hyperspectral remote sensing field. Deep learning is an efficient learning algorithm that has been recently applied to HSI classification. This paper proposes a new spectralspatial HSI classification method based on the deep features extraction using stacked-auto-encoders (SAE) and unsupervised HIS segmentation. Specifically, first the SAE model is exploited as a classical spectral information-based classifier to extract the deep features. Second, spatial dominated information is extracted by using effective boundary adjustment based segmentation technique. Finally, maximum voting criteria is used to merge the extracted spectral and spatial features, which results into the accurate spectral-spatial HSI classification. Experimental results with widely-used hyperspectral data confirms that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies.