Stereo matching based on urban satellite remote sensing image pair

被引：0

作者：

Zhao J. ^{[1
,2
]}

Chen X. ^{[1
,2
]}

Hou W. ^{[1
,2
]}

Han J. ^{[1
,2
]}

机构：

[1] School of Optics and Photonics, Beijing Institute of Technology, Beijing

[2] Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing Institute of Technology, Beijing

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 07期

关键词：

Census transform; Disparity optimization; Edge constraint; Satellite remote sensing; Stereo matching; Urban remote sensing image;

D O I：

10.37188/OPE.20223007.0830

中图分类号：

学科分类号：

摘要：

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. © 2022, Science Press. All right reserved.

引用

页码：830 / 839

页数：9

共 18 条

[1]

WANG Y Q, LI H Y., Three-dimensional reconstruction of mapping satellite-1 imagery using semi-global matching algorithm based on mutual information, Forest Resources Management, 4, pp. 134-139, (2016)

[2]

SCHARSTEIN D, SZELISKI R, ZABIH R., A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV2001), pp. 131-140, (2001)

[3]

HAMZAH R A, IBRAHIM H., Literature survey on stereo vision disparity map algorithms, Journal of Sensors, 2016, pp. 1-23, (2016)

[4]

ZABIH R, WOODFILL J., Non-parametric local transforms for computing visual correspondence, Computer Vision-ECCV '94, pp. 151-158, (1994)

[5]

LI H CH, LI Y, GU ZH, Stereo matching method for lunar rover using the plane constraint, Opt. Precision Eng, 27, 2, pp. 433-439, (2019)

[6]

TATAR N, AREFI H., Evaluation of selected cost aggregation methods on high resolution satellite stereo images[J], The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 1045-1050, (2019)

[7]

HIRSCHMULLER H., Stereo processing by semiglobal matching and mutual information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 2, pp. 328-341, (2008)

[8]

EINECKE N, EGGERT J., A two-stage correlation method for stereoscopic depth estimation, 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 227-234, (2010)

[9]

GUO S Y., Research on Three-dimensinoal Reconstruction Technology Based on Satellite Remote Sensing Images, (2018)

[10]

ZHAO L, LIU Y M, MEN C G, Et al., Double propagation stereo matching for urban 3-D reconstruction from satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, 8144, 99, pp. 1-17

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