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Detection of meso-micro scale surface features based on microcanonical multifractal formalism |
Yuanyuan Yang(杨媛媛)1, Wei Chen(陈伟)2, Tao Xie(谢涛)3, William Perrie4 |
1 School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; 2 School of Automation, Wuhan University of Technology, Wuhan 430070, China; 3 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China; 4 Fisheries & Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, B2Y 4A2 Canada |
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Abstract Synthetic aperture radar (SAR) is an effective tool to analyze the features of the ocean. In this paper, the microcanonical multifractal formalism is used to analyze SAR images to obtain meso-micro scale surface features. We use the Sobel operator to calculate the gradient of each point in the image, then operate continuous variable scale wavelet transform on this gradient matrix. The relationship between the wavelet coefficient and scale is built by linear regression. This relationship decides the singular exponents of every point in the image which contain local and global features. The manifolds in the ocean can be revealed by analyzing these exponents. The most singular manifold, which is related to the streamlines in the SAR images, can be obtained with a suitable threshold of the singular exponents. Experiments verify that application of the microcanonical multifractal formalism to SAR image analysis is effective for detecting the meso-micro scale surface information.
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Received: 31 July 2017
Revised: 12 September 2017
Accepted manuscript online:
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PACS:
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05.45.Df
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(Fractals)
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41.20.-q
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(Applied classical electromagnetism)
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Fund: Project supported by the National Key R&D Program of China (Grant No.2016YFC1401007),the Global Change Research Program of China (Grant No.2015CB953901),the National Natural Science Foundation of China (Grant No.41776181),the Canadian Program on Energy Research and Development (OERD),Canadian Space Agency's SWOT Program,and the Canadian Marine Environmental Observation Prediction and Response Network (MEOPAR). |
Corresponding Authors:
Yuanyuan Yang
E-mail: yangyuanyuan@whut.edu.cn
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Cite this article:
Yuanyuan Yang(杨媛媛), Wei Chen(陈伟), Tao Xie(谢涛), William Perrie Detection of meso-micro scale surface features based on microcanonical multifractal formalism 2018 Chin. Phys. B 27 010502
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