Texture-based superpixel segmentation algorithm for classification of hyperspectral images

To increase classification accuracy, a variety of feature extraction techniques have been presented. A pre-processing method called superpixel segmentation divides an image into meaningful sub-regions, which simplifies the image. This substantially reduces single-pixel misclassification. In this work, a texture-based superpixel segmentation technique is developed for the accurate classification of hyperspectral images (HSI). Initially, the local binary pattern and Gabor filters are employed to extract local and global image texture information. The extracted texture features are then provided as input to the simple linear iterative clustering (SLIC) algorithm for segmentation map generation. The final classification map is constructed by utilising a majority vote strategy between the superpixel segmentation map and the pixel-wise classification map. The proposed method was validated on standard HSI datasets. In terms of classification performance, it outperformed other state-of-the-art algorithms. Furthermore, the algorithm may be incorporated into the UAV's onboard camera to automatically classify HSI.