Building Height Extraction from GF-7 Satellite Images Based on Roof Contour Constrained Stereo Matching

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-04, Vol.14 (7), p.1566
• Main Authors: Zhang, Chenni, Cui, Yunfan, Zhu, Zeyao, Jiang, San, Jiang, Wanshou
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; building height estimation; Buildings; Construction; contour constrained matching; Contour matching; Cultural heritage; Deep learning; Elevation; GF-7 satellite; High rise buildings; Histograms; Lidar; Methods; Performance evaluation; podium segmentation; Remote sensing; Roofs; Root-mean-square errors; Satellite imagery; Satellites; Standard deviation; Statistical analysis; urban 3D reconstruction
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14071566

Building height is one of the basic geographic information for planning and analysis in urban construction. It is still very challenging to estimate the accurate height of complex buildings from satellite images, especially for buildings with podium. This paper proposes a solution for building height estimation from GF-7 satellite images by using a roof contour constrained stereo matching algorithm and DSM (Digital Surface Model) based bottom elevation estimation. First, an object-oriented roof matching algorithm is proposed based on building contour to extract accurate building roof elevation from GF-7 stereo image, and DSM generated from the GF-7 stereo images is then used to obtain building bottom elevation. Second, roof contour constrained stereo matching is conducted between backward and forward image blocks, in which the difference of standard deviation maps is used for the similarity measure. To deal with the multi-height problem of podium buildings, the gray difference image is adopted to segment podium buildings, and re-matching is conducted to find out their actual heights. Third, the building height is obtained through the elevation difference between the building top and bottom, in which the evaluation of the building bottom is calculated according to the elevation histogram statistics of the building buffer in DSM. Finally, two GF-7 stereo satellite images, collected in Yingde, Guangzhou, and Xi’an, Shanxi, are used for performance evaluation. Besides, the aerial LiDAR point cloud is used for absolute accuracy evaluation. The results demonstrate that compared with other methods, our solution obviously improves the accuracy of height estimation of high-rise buildings. The MAE (Mean Absolute Error) of the estimated building heights in Yingde is 2.31 m, and the MAE of the estimated elevation of building top and bottom is approximately 1.57 m and 1.91 m, respectively. Then the RMSE (Root Mean Square Error) of building top and bottom is 2.01 m and 2.57 m. As for the Xi’an dataset with 7 buildings with podium out of 40 buildings, the MAE of the estimated building height is 1.69 m and the RMSE is 2.34 m. The proposed method can be an effective solution for building height extraction from GF-7 satellite images.