Hyperspectral image classification based on morphological profiles and decision fusion

Extended morphological profile (EMP) is an important mathematical tool for extracting structural information from the hyperspectral images. However, the accuracy of the EMP-based classification is greatly influenced by the choice of structuring element (SE). In this article, two supervised classification frameworks multiclassifier system with morphological profiles (MCSMP) and MCSMP2 are proposed that exploit rich spectral and structural information of hyperspectral images using EMPs and multiclassifier system for better classification than conventional methods. The EMPs with SEs of multiple shapes are used instead of one particular shape to better detect the response from the structures in the image. The EMPs created from SEs of different shapes are independently classified followed by decision fusion to generate final classification map. The classification results are compared with the conventional pixelwise and other EMP-based methods. The experimental results from three different types of hyperspectral data sets demonstrate that the proposed methods have significantly improved the spectral approach and outperformed the other studied methods in terms of classification accuracy. The new methods are more robust to the noise and produce good classification accuracy with very limited training samples. Various decision fusion techniques are evaluated, which performed differently in tested scenarios. Two different classifiers, Support Vector Machine (SVM) and random forest, are used in the experiments. It is shown that the proposed methods perform better with random forest classifier.