Pixel Classification of Hyperspectral Images based on Spectral and Gabor Texture Features

Hyperspectral images consist of rich spectral information and spatial structure information that can be fully employed to implement the ground objects classification effectively. Due to the strong representation ability of Gabor filter to extract relevant features at different scales and directions, this paper proposes a Gabor filter group with 5 wavelengths and 4 directions that is exploited to the two-dimensional image of each band. To be specific, the 3×3 local neighborhood mean square error of the filtered image pixel is used as the texture feature of the pixel in a certain filtered image, and the texture features of all the filtered images in each band are combined together as the overall texture features of this pixel. XGBoost is leveraged as the classifier to carry out the classification experiments based on spectral feature, Gabor texture feature, and the combined features of the spectral and Gabor texture features, respectively. The experimental results show that our proposal can obtain the accuracy of , and based on only used spectral features, only used texture features, and the combined features, respectively. It is obvious that the classification accuracy can be significantly enhanced based on the combined features of the spectral and Gabor texture features.