Automatic Generation of Standard Deviation Attribute Profiles for Spectral–Spatial Classification of Remote Sensing Data

Extended attribute profiles, which are based on attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding attribute filters. In this letter, we present a technique to automatically build the extended attribute profiles with the standard deviation attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.