Satellite Image Classification Using HMM with Whale Optimization

Abstract

The purpose of this paper, the proposal  a new method that combining  the  Hidden Markov Model (HMM) and Whale Optimization together in remote sensing to resolve the land cover issue by splitting  the unsupervised satellite image using threshold value. The HMM helps in extraction of texture modeling and segmentation such as classification. The majority of the model contextual dependencies and the absorption of noise. To test the proposed method a very high resolution remote sensing image was used, acquired by a world view - 4. The results show the best performance of the proposed method related to the problem through image segmentation. The processing time is low in relative with the faster convergence.   Results of experiment specify that HMM-WOA generates higher precision than the existing method. Therefore, it gives an unsupervised effective algorithm for the remote sensing image of classification.