Spectral-spatial classification of hyperspectral image based on semi-supervised and level set methods

A new scheme integrating segmentation into classification to analyze hyperspectral images is presented in this paper, particularly for images with a very few number of labels and largely adjacent spatial structures. Using pixel-wise semi-supervised support vector machine, the image is classified, and segmented by modified C-V level set in this method. Afterwards, classification and segmentation images are combined with neighborhood voting. Experiments are conducted on a 200-band AVIRIS image of the Northwestern Indiana´s Indian Pine site. The integration of the spatial information from the level set segmentation provides classification images with more homogeneous regions and improves the classification accuracy, comparing to the general pixel-wise supervised and semi-supervised classification.