Automatic Spectral–Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction

Abstract

A robust framework for the classification of hyperspectral images which takes into account both spectral and spatial information is proposed. The extended multivariate attribute profile (EMAP) is used for extracting spatial information. Moreover, for solving the so-called curse of dimensionality, supervised feature extraction is carried out on both the original hyperspectral data and the output of the EMAP. After performing the dimensionality reduction, two output vectors of the original data and attributes are concatenated into one stacked vector. The final classification map is achieved by using a random-forest classifier. The main difficulties of using an EMAP is to initialize the attribute parameters. Therefore, a fully automatic scheme of the proposed method is introduced to overcome the shortcomings of using EMAP. The proposed method is tested on two widely known data sets. Experimental results confirm that the proposed method provides an accurate classification map in an acceptable CPU processing time.