Land cover classification combining Sentinel-1 and Landsat 8 imagery driven by Markov random field with amendment reliability factors

Abstract

Reliability factors in Markov random field (MRF) could be used to improve classification performance for synthetic aperture radar (SAR) and optical images; however, insufficient utilization of reliability factors based on characteristics of different sources leaves more room for classification improvement. To solve this problem, a Markov random field (MRF) with amendment reliability factors classification algorithm (MRF-ARF) is proposed. The ARF is constructed based on the coarse label field of urban region, and different controlling factors are utilized for different sensor data. Then, ARF is involved into the data energy of MRF, to classify the sand, vegetation, farmland, and urban regions, with the gray level co-occurrence matrix textures of Sentinel-1 imagery and the spectral values of the Landsat 8 imagery. In the experiments, Sentinel-1 and Landsat-8 images are used with overall accuracy and Kappa coefficient to evaluate the proposed algorithm with other algorithms. Results show that the overall accuracy of the proposed algorithm has the superiority of about 20% in overall precision and at least 0.2 in Kappa coefficient than the comparison algorithms. Thus, the problem of insufficient utilization of different sensors data could be solved.