Open AccessResearch on Building Height Extraction Method from High-resolution Image
Author(s) -
Hao Tang,
Linlin Du,
Chun Wang,
Pengfei Li,
Sheng Ge,
Dezhuang Meng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1961/1/012062
Subject(s) - shadow (psychology) , computer science , remote sensing , image (mathematics) , artificial neural network , artificial intelligence , extraction (chemistry) , complement (music) , high resolution , computer vision , pattern recognition (psychology) , geography , psychology , biochemistry , chemistry , chromatography , complementation , psychotherapist , gene , phenotype
In recent years, there are many researches on calculating the height of buildings by extracting the shadow of buildings, but this method is difficult to be realized in the case of dense buildings or complex ground. To solve this problem, we can use the side information of the building to retrieve the height of the building. In this paper, Otsu Algorithm and LVQ neural network are used to extract the side information of buildings from high-resolution remote sensing images, calculate its length, and then calculate the height of the building according to the physical model of imaging. Compared with the measured building height, the calculation results of this method can meet the accuracy requirements of building height calculation, and this method can complement the results of shadow extraction method. Finally, the height information of most buildings in the area can be obtained.