Semi-supervised classification of hyperspectral image using random forest algorithm

Abstract

This paper presents a hyperspectral image classification method based on the semi-supervised random forest (SSRF) algorithm. This method uses Deterministic Annealing (DA) and the random forest classifier (RFC). The first step consists of performing the random forest algorithm by using labeled data. Then, image is classified and the probability of each unlabeled data will be computed. Based on the probability and the temperature parameter, label of unlabeled data will be determined. Finally, the classification is carried out based on the labeled data and unlabeled data which were converted to labeled data in the procedure of algorithm. The proposed method and also a conventional RFC method have been applied to an APEX (Airborne Prism Experiment) hyperspectral image. The results show more consistency in homogeneous area. In addition, its overall accuracy of classification is 82.63%, while the kappa coefficient is 0.78, and both are higher than the accuracies of spectral based classification using the conventional RFC, i.e. 73.58% and 0.68 respectively.