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. [Download paper here](https://ieeexplore.ieee.org/abstract/document/7450160/) Recommended citation: Your Name, You. (2015). "Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach." IEEE Transactions on Geoscience and Remote Sensing. 54(8).
