An Adaptive Sampling Strategy Based on Region Growing for Near-Field-Based Imaging of Radiation Sources
Sihong Tao, Huapeng Zhao, Zhizhang Chen
Abstract
Near-field-based imaging methods have been widely used to construct images of complex objects. A large number of near-field samples are needed to obtain an accurate image, resulting in high implementation expenditures. By utilizing the fact that the electric field varies fast near strong sources, this paper proposes an adaptive sampling strategy to add near field sampling points near the strong sources adaptively. With the proposed strategy, the initial uniform sampling is conducted to obtain rough source data. Then the region growing method is applied to identify multiple strong sources. More sampling points are added around every strong source point to extract all the source information accurately. The experiment results demonstrate that the proposed strategy can effectively reconstruct radiation sources using significantly less time than the existing methods.