Hyperspectral image classification based on spectral and geometrical features
In this paper, we propose to integrate geometrical features, such as the characteristic scales of structures, with spectral features for the classification of hyperspectral images. The spectral features which only describe the material of structures can not distinguish objects made by the same material but with different semantic meanings (such as the roofs of some buildings and the roads). The use of geometrical features is therefore necessary. Moreover, since the dimension of a hyperspectral image is usually very high, we use linear unmixing algorithm to extract the endmemebers and their abundancemaps in order to represent compactly the spectral information. Afterwards, with the help of these abundance maps, we propose a method based on topographic map of images to estimate local scales of structures in hyperspectral images. The experiment shows that the geometrical features can improve the classification results, especially for the classes made by the same material but with different semantic meanings. When compared to the traditional contextual features (such as morphological profiles), the local scale feature provides satisfactory results without considerably increasing the feature dimension.