An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image

With the development of remote sensing technology, hyperspectral sensors can record reflectance of ground objects in hundreds of bands, which undoubtedly brings great benefits for hyperspectral remote sensing image that contains abundant information of the land covers. At present, some hyperspectral images are contaminated by the noise and a lack of spatial information, this phenomenon hinders the improvement of classification accuracy. To further mine the spatial features of hyperspectral image, we propose an effective feature extraction method based on Spectral-Gabor space discriminant analysis (SGDA). The framework of SGDA is roughly divided into four steps: firstly, we obtain p_i {p_i|1≤ i更多