计算机科学
人工智能
正射影像
计算机视觉
相似性(几何)
匹配(统计)
影子(心理学)
支持向量机
模式识别(心理学)
数学
图像(数学)
统计
心理学
心理治疗师
作者
Guoqing Zhou,Yi Tang,Wenxi Zhang,Weiguang Liu,Yue Jiang,Ertao Gao,Qiaoming Zhu,Yuhang Bai
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-20
被引量:5
标识
DOI:10.1109/tgrs.2023.3294531
摘要
Shadow detection and compensation on high-resolution orthophoto is one of the most important tasks for ensuring high-quality of the radiometric balance of digital orthophoto map (DOM). This paper proposes a novel shadow detection method through semantic matching between the artificial “shadow” polygons (ASPs) and the real shadowed polygons (RSPs). The ASPs are created by digital building model (DBM), solar zenith and solar azimuth. A group of polygons semantic features, such as position similarity, area similarity, direction similarity and shape similarity are described and used as matching parameters, and then the real shadow regions (polygons) (RSPs) are detected using two-level matchings between the ASPs and the RSPs. The initial of matching aims at obtaining the initial probability and the candidates of the matching pair through determining the initial search circle cantered at the ASPs. The final of matching aims at finding the final match pairs through iteration of semantic matching, of which the maximum probability, which is calculated by the support coefficient of the adjacent match pair, is adopted as the criterion of the iteration. The experimental area located in Denver, Colorado, USA is used to validate the proposed algorithm. When compared with the dual-threshold, the Least-Squares Support Vector Machine (LS-SVM), and the UNet shadow detection method, the method proposed in this paper is able to increase the rate of shadow detection by 38.98%, 17.33%, and 13.14%, and decrease the fault detection rate by 19.43%, 10.01%, and 4.57%.
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