A multiscale modified minimum spanning forest method for spatial-spectral hyperspectral images classification

Abstract This paper aimed to present a new method for the spectral-spatial classification of hyperspectral images, based on the idea of modified minimum spanning forest (MMSF). MMSF works on the obtained regions of pre-segmentation step that are considered as nodes of an image graph. In the proposed method, the image is first smoothed by the multiscale edge-preserving filter (MSEPF) and then the MMSF is built in each scale. Finally, all the classification maps of each scale are combined with a majority vote rule. The suggested method, named as MSEPF-MMSF, is performed on four hyperspectral images with different properties, and the experiments deal with the impacts of parameters of filter and the number of markers. The results demonstrate that the proposed method has improved the classification accuracies with respect to the previous methods