Registration Algorithm Based on Line-Intersection-Line for Satellite Remote Sensing Images of Urban Areas

被引:10
作者
Liu, Siying [1 ,2 ,3 ]
Jiang, Jie [1 ,2 ,3 ]
机构
[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China
[2] Educ Minist, Precis Optomechatron Technol Key Lab, Beijing 100191, Peoples R China
[3] Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
基金
中国国家自然科学基金;
关键词
image registration; line-intersection-line (LIL); remote sensing; urban areas; background variations; MATCHING ALGORITHM; SEGMENT DETECTOR; DESCRIPTOR; MODEL;
D O I
10.3390/rs11121400
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Image registration is an important step in remote sensing image processing, especially for images of urban areas, which are often used for urban planning, environmental assessment, and change detection. Urban areas have many artificial objects whose contours and edges provide abundant line features. However, the locations of line endpoints are greatly affected by large background variations. Considering that line intersections remain relatively stable and have high positioning accuracy even with large background variations, this paper proposes a high-accuracy remote sensing image registration algorithm that is based on the line-intersection-line (LIL) structure, with two line segments and their intersection. A double-rectangular local descriptor and a spatial relationship-based outlier removal strategy are designed on the basis of the LIL structure. First, the LILs are extracted based on multi-scale line segments. Second, LIL local descriptors are built with pixel gradients in the LIL neighborhood to realize initial matching. Third, the spatial relations between initial matches are described with the LIL structure and simple affine properties. Finally, the graph-based LIL outlier removal strategy is conducted and incorrect matches are eliminated step by step. The proposed algorithm is tested on simulated and real images and compared with state-of-the-art methods. The experiments prove that the proposed algorithm can achieve sub-pixel registration accuracy, high precision, and robust performance even with significant background variations.
引用
收藏
页数:26
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