A fusion based land cover classification model using remote sensed images

Abstract

Classification of land cover from remote sensed image is quite challenging task. Since the satellite images preserve spatial and spectral information, thus it is essential to identify the land cover classes and classify them to generate the thematic map. The remote sensed images and thus produced thematic maps are useful for extracting the esteemed information in diagnosing, supervising, and management of earth’s surface. In this paper, a multiclass land cover classification model is proposed that comprise of pre-processing method, a multiclass classifier and performance evaluation strategy. The land cover-based satellite images are applied to this model to generate a land cover map labelled with seven land cover classes. The morphological opening, closing, and a fusion technique are involved in pre-processing stage to extract the spatial information as well as reduce the incurred noise from the input image. Then a supervised classification methodology is introduced to classify the image into 7 number of land cover classes based on the spectral values of each pixel of the image. The overall achievement of the proposed model is compared with some existing multiclass supervised and unsupervised classification techniques such as Naïve Bayes classifier (NBC), Decision tree (DT), K-nearest neighbour (KNN), Convolution Neural Network (CNN).