基于偏微分方程的遥感图像目标提取
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香港理工大学,香港理工大学

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Ministry of Science and Technology of China (Project No.: 2012BAJ15B04), Research Grants Council of Hong Kong (Project No.: PolyU 15223015, PolyU 1-ZEA5, and PolyU 5249/12E), National Natural Science Foundation of China (Project No.: 41331175), Leading Talent Project of the National Administration of Surveying (Project No.: K.SZ.XX.VTQA).


Partial differential equation-based object extraction from remote sensing imagery
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The Hong Kong Polytechnic University,The Hong Kong Polytechnic University

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    摘要:

    从遥感图像中提取感兴趣的目标是遥感和地学领域的一个重要任务.先前的研究主要集中于目标提取的精度,而很少关注目标提取的效率.因此,作者提出一个基于偏微分方程的框架来进行半自动多类目标提取.首先,作者对水平集方法,非线性扩散,以及活动轮廓之间的数学关系进行了深入的探究.从探究的结果作者发现基于边缘和基于区域的偏微分方程在目标提取中同等重要,因此作者把它们概括成一个统一的框架.接着,为了使计算更加高效,作者用尺度空间滤波替换传统的曲率归一项.最后,作者通过一系列实验证明了该方法的有效性.

    Abstract:

    Object extraction is an essential task in remote sensing and geographical sciences. Previous studies mainly focused on the accuracy of object extraction method while little attention has been paid to improving their computational efficiency. For this reason, a partial differential equation (PDE)-based framework for semi-automated extraction of multiple types of objects from remote sensing imagery was proposed. The mathematical relationships among the traditional PDE-based methods, i.e., level set method (LSM), nonlinear diffusion (NLD), and active contour (AC) were explored. It was found that both edge- and region-based PDEs are equally important for object extraction and they are generalized into a unified framework based on the derived relationships. For computational efficiency, the widely used curvature-based regularizing term is replaced by a scale space filtering. The effectiveness and efficiency of the proposed methods were corroborated by a range of promising experiments.

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李仲玢,史文中.基于偏微分方程的遥感图像目标提取[J].红外与毫米波学报,2016,35(3):257~262]. LI Zhong-Bin, SHI Wen-Zhong. Partial differential equation-based object extraction from remote sensing imagery[J]. J. Infrared Millim. Waves,2016,35(3):257~262.]

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  • 收稿日期:2015-04-28
  • 最后修改日期:2015-12-01
  • 录用日期:2015-12-04
  • 在线发布日期: 2016-07-28
  • 出版日期: 2016-07-28
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