Wetland information extraction of remote sensing imagery based on Markov random field theory

Dengrong Zhang; Yang Wu

doi:10.1117/12.910425

15 August 2011 Wetland information extraction of remote sensing imagery based on Markov random field theory

Dengrong Zhang, Yang Wu

Proceedings Volume 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing; 820318 (2011) https://doi.org/10.1117/12.910425
Event: Seventeenth China Symposium on Remote Sensing, 2010, Hangzhou, China

Abstract

Due to the indistinction of land boundary and the confusion of categories in wetland as well as the big spectral difference of high-resolution remote sensing images, how to segment land boundaries exactly and maintain homogeneity in one category as much as possible are the difficult points of wetland information extraction of remote sensing images. In this paper, Xixi Wetland in Hangzhou is taken as research object and QuickBird high-resolution image as research data. Two approaches for wetland information accurate extraction based on Markov random field (MRF) theory are explored. The experimental results showed that this method has a good effect on exact segmentation of land boundaries and Inhibition of classification noises, and has higher accuracy and speed compared with other MRF methods.

Citation Download Citation

Dengrong Zhang and Yang Wu "Wetland information extraction of remote sensing imagery based on Markov random field theory", Proc. SPIE 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing, 820318 (15 August 2011); https://doi.org/10.1117/12.910425

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KEYWORDS

Magnetorheological finishing

Image classification

Remote sensing

Image segmentation

Vegetation

Image resolution

Chromium

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