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A New Algorithm Based on Hybrid Feature Space MRF(MarkovRandom Filed)Model for Water Information Extractionfrom High Resolution Remote Sensing Imagery(PDF)


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A New Algorithm Based on Hybrid Feature Space MRF(MarkovRandom Filed)Model for Water Information Extractionfrom High Resolution Remote Sensing Imagery
Li ShijinWang Shengte
College of Computer and Information,Hohai University,Nanjing 210098,China
water body extractionMRF modelnormalized difference water index(NDWI)hybrid feature spaceGraph Cut
Water information extraction in remote sensing images is an important application of remote sensing technology in water resources surveying and utilization,detection of water ecology change and other aspects. The existing water extraction methods as water index or image classification are not accurate enough for water boundary treatment,and they are easy to produce the problem of error extraction and leakage extraction. Based on the existing algorithms that constructing water index to extract water information,we have proposed a new algorithm which combine image segmentation algorithm based on MRF model with normalized difference water index(NDWI)for extraction of water information. We represent the pixels in a remote sensing image as random variables in an MRF model,and introduce hybrid feature in the energy function on these variables,minimize the energy function to find the optimal water boundary,by using an iterative graph cut scheme. Then water boundary is adaptively refined according to the water index and color feature of the extracted water body. The experiment of water information extraction in Shilianghe Reservoir shows that our approach can achieve significant accuracy as it automatically adapts to the extraction of water information in reservoir whose surroundings are complicated and the boundary of water bodies is handled precisely.


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