A hybrid colour model based land cover classification using random forest and support vector machine classifiers

Abstract

Land cover monitoring using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover and land use categories. Classification is a supervised learning method which maps a data item into predefined classes. Colour is an important feature used in image classification since humans tend to distinguish images mostly based on colour feature. This paper proposes a hybrid colour model for land cover classification in which colour features are extracted by combining the hue (H) values of HSV colour space and luminance (L) values of LUV colour space. The extracted features are trained and tested with random forest (RF) and support vector machine (SVM) classifiers. The performance of the proposed hybrid colour model is compared with the existing HSV colour space model using RF and SVM classifiers based on several metrics such as accuracy, sensitivity, specificity and f-score. Hyper spectral dataset of Pavia University and an IRS LISS IV orthorectified dataset are chosen as the input image for this experiment.