Spectral Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach
In this paper, we propose a spectral spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant embedding algorithm is proposed for spectral feature extraction from high dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple feature based classifier is trained for image classification. Experimental results on well known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
IEEE Transactions on Geoscience and Remote Sensing