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).
The Hong Kong Polytechnic University,The Hong Kong Polytechnic University
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.
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.]复制