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.
Highlights
The availability of reliable land cover information is of great importance for many earth scientific applications, such as the transition of land, increasing demand for food and fiber, biodiversity, and climate [1,2,3,4,5]
A classification algorithm based on Markov random field (MRF) with amendment reliability factors is proposed
Based on the coarse urban label field, the additional controlling factors are involved in reliability factors to construct the amendment reliability factors
Summary
The availability of reliable land cover information is of great importance for many earth scientific applications, such as the transition of land, increasing demand for food and fiber, biodiversity, and climate [1,2,3,4,5]. Optical remote sensing data could be affected by unfavorable weather condition, and there are some difficulties related to the so-called spectral confusions that lower the classification accuracy [10]. Synthetic aperture radar (SAR) as an active remote sensing technique can capture information in all-day, and even in unfavorable weather conditions, but could not provide spectral information, resulting in difficulties in image interpretation [11]. The joint usage SAR and optical data have been adopted in many applications [12]. In this regard, combination of two kinds of data could be utilized to improve good performance on land cover classification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
