摘要
为了解决城市卫星遥感影像中存在大量阴影以及大量的视差阶跃区域导致立体匹配效果不佳的问题,提出一种适应于城市遥感影像对的立体匹配算法。对算法所使用的匹配代价函数、代价聚合方法和视差优化方法等进行研究,改进了匹配代价函数,利用多阶加权Census算法减小噪声等因素的影响。在代价聚合中加入建筑边缘信息的约束条件,在视差细化部分充分考虑城市建筑的形态特点,对视差图进行优化。实验结果表明:在MiddleBury数据集上,本文算法的准确率比经典SGM算法提升4.54%;在城市区域WorldView-2立体影像对上,建筑屋顶的高程方差为0.71,满足基于城市卫星遥感影像对获取高精度视差图的要求,能够为城市三维重建提供良好的条件。
To solve the problem of poor stereo matching effect owing to numerous shadows and disparity step regions in urban satellite remote sensing images,a stereo matching algorithm suitable for urban remote sensing image pairs was proposed. The matching cost function,cost aggregation method,disparity,and optimization method used by the algorithm were investigated. First,the matching cost function was improved and the multi-order weighted census algorithm was used to reduce the influence of noise and other factors. Subsequently,the constraints of the building edge information were added to the cost aggregation. Finally,regarding disparity refinement,the disparity map was optimized by fully considering the characteristics of urban building morphology. The experimental results show that on the Middlebury dataset,the accuracy of this algorithm is 4. 54% higher than that of the classic SGM algorithm. On the WorldView-2 stereo image pair in the urban area,the variance of the building roof elevation is 0. 71. The requirements to obtain high-precision disparity maps are met based on urban satellite remote sensing images and good conditions for urban three-dimensional reconstruction are provided.
作者
赵杰
陈小梅
侯玮旻
韩嘉威
ZHAO Jie;CHEN Xiaomei;HOU Weimin;HAN Jiawei(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education of China,Beijing Institute of Technology,Beijing 100081,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2022年第7期830-839,共10页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.61875013)。
关键词
卫星遥感
城市遥感影像
Census变换
边缘约束
视差优化
立体匹配
satellite remote sensing
urban remote sensing image
census transform
edge constraint
disparity optimization
stereo matching