Abstract-A new spectral-spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation. The output of these two methods is classified by support vector machines. Experimental results indicate that the integration of the two segmentation methods can overcome the drawbacks of each other and increase the overall accuracy in classification. Index Terms-Hyperspectral image analysis, mean shift segmentation, multilevel segmentation.
Ghamisi et al. REF introduced two main segmentation approaches for classification of hyperspectral images.
Integration of Segmentation Techniques for Classification of Hyperspectral Images
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters
Mathematics Computer Science