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A platform for research: civil engineering, architecture and urbanism
Remote sensing recognition of roof area of flat-roofed houses based on Gram Schmit algorithm
Aiming at the problem that existing remote sensing recognition methods for the roof area of flat roofed houses are difficult to obtain accurate boundaries of the roof, a remote sensing recognition method for the roof area of flat roofed houses based on Gram Schmit algorithm is proposed. Perform geometric correction on the collected remote sensing images of the roofs of flat roofed houses using polynomial modeling methods; By using DeepLab semantic segmentation technology to extract the boundary feature parameters of the roof of a flat roofed house, and applying the Gram Schmit algorithm to further identify and outline the contour of the roof of the flat roofed house, the roof area can be calculated. The experimental results show that this method can effectively improve the accuracy and efficiency of identifying the roof area of flat roofed houses.
Remote sensing recognition of roof area of flat-roofed houses based on Gram Schmit algorithm
Aiming at the problem that existing remote sensing recognition methods for the roof area of flat roofed houses are difficult to obtain accurate boundaries of the roof, a remote sensing recognition method for the roof area of flat roofed houses based on Gram Schmit algorithm is proposed. Perform geometric correction on the collected remote sensing images of the roofs of flat roofed houses using polynomial modeling methods; By using DeepLab semantic segmentation technology to extract the boundary feature parameters of the roof of a flat roofed house, and applying the Gram Schmit algorithm to further identify and outline the contour of the roof of the flat roofed house, the roof area can be calculated. The experimental results show that this method can effectively improve the accuracy and efficiency of identifying the roof area of flat roofed houses.
Remote sensing recognition of roof area of flat-roofed houses based on Gram Schmit algorithm
Leng, Lu (editor) / Yuan, Hui (editor) / Shen, Yulan (author) / Chen, Meng (author) / Yin, Sijia (author) / Xu, Pengfei (author) / Chu, Hongfei (author) / Chen, Chaoyang (author)
Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024) ; 2024 ; Chengdu, China
Proc. SPIE ; 13486
2025-01-09
Conference paper
Electronic Resource
English
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