An improved classification method for hyperspectral data based on spectral and morphological information

Abstract

Combining spectral and morphological information to classify hyperspectral data offers a considerable advantage over methods based solely on spectral or morphological data. Previously, a classification method was proposed that concatenated extended morphological profile (EMP) and spectral information into one feature vector for each pixel of an image. Although this method has the merit of simultaneously using spectral and morphological information, it runs the risk of generating new spectral constituents (not present in the original image). In this letter, an improved classification method based on fusing extended multivariate morphological profile (EMMP) and spectral information was proposed. A new vector ordering method based on spectral purity-based criterion was adopted to overcome the problem of generating new constituents in EMP. A feature selection algorithm was employed to improve the efficiency of EMMP. Experiments were carried out on a hyperspectral data set collected by NASA's Airborne Visible-Infrared Imaging Spectrometer sensor. Experimental results showed that EMMP was effective at describing morphological information in hyperspectral data, and that this letter's method was superior to the previous method in terms of classification accuracy but inferior to the previous method in terms of time consumption.