TY - JOUR
T1 - 基于城市遥感卫星影像对的立体匹配
AU - Zhao, Jie
AU - Chen, Xiaomei
AU - Hou, Weimin
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2022, Science Press. All right reserved.
PY - 2022/4/10
Y1 - 2022/4/10
N2 - 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.
AB - 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.
KW - Census transform
KW - Disparity optimization
KW - Edge constraint
KW - Satellite remote sensing
KW - Stereo matching
KW - Urban remote sensing image
UR - http://www.scopus.com/inward/record.url?scp=85129376041&partnerID=8YFLogxK
U2 - 10.37188/OPE.20223007.0830
DO - 10.37188/OPE.20223007.0830
M3 - 文章
AN - SCOPUS:85129376041
SN - 1004-924X
VL - 30
SP - 830
EP - 839
JO - Guangxue Jingmi Gongcheng/Optics and Precision Engineering
JF - Guangxue Jingmi Gongcheng/Optics and Precision Engineering
IS - 7
ER -