Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter

We propose a spatial-spectral hyperspectral image classification method based on multiscale superpixels and guided filter (MSS-GF). In order to use spatial information effectively, MSSs are used to get local information from different region scales. Sparse representation classifier is used to generate classification maps for each region scale. Then, multiple binary probability maps are obtained for each of the classification maps. Adding GF denoises the classification results and then improves the classification accuracy. Finally, the class label of each pixel is determined by majority voting rule.