Efficient building category classification with fAÇade information from oblique aerial images

Xiao，C.1,3; Xie，X.2,3; Zhang，L.4; Xue，B.2,3

10.5194/isprs-archives-XLIII-B2-2020-1309-2020

2020-08-06

1682-1750

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

43

B2

1309-1313

Building category refereed to categorizing structures based on their usage is useful for urban design and management and can provide indexes of population, resource and environment related problems. Currently, the statistics are mainly collected by manual from street data or roughly extracted from remote sensing data which are either laborious or too coarse. With remote sensing data (e.g. satellite and aerial images), buildings can be automatically identified from the top-view, but the detailed categories of single buildings are not recognized. Façade from oblique-view image can greatly help us to identify the categories of buildings, for example, balcony usually exist in resident buildings. Hence, in this paper, we propose an efficient way to classify building categories with the façade information. Firstly, following the texture mapping procedure, each building's façade textures are cropped from oblique images via a perspective transformation. Then, the average colour, the stander deviation in R, G, B channel, and the rectangle Haar-like features are extracted and feed to a further random forest classifier for their category identifications. In the experiment, we manually selected 262 building façades that can be classified into four functional types as: 1) regular residence ; 2) educational building; 3) office ; 4) condominium. The results shows that, with 30% data as training samples, the classification accuracy can reach 0.6 which is promising in real applications and we believe with more sophisticated feature descriptors and classifiers, e.g., neuronal networks, the accuracy can be much higher.

Building Category Classification Fac ade Oblique Aerial Images Remote Sensing

其他

英语

EI

20203809212034

Image classification ; Textures ; Buildings ; Decision trees ; Antennas ; Classification (of information) ; Neurons

Buildings and Towers:402 ; Biology:461.9 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Information Sources and Analysis:903.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Systems Science:961

2-s2.0-85091116558

Scopus

1.Artificial Intelligence and Earth Perception Research Center,School of Automation Engineering,University of Electronic Science and Technology of China,China
2.Key Lab of Pollution Ecology and Environmental Engineering,Institute of Applied Ecology,Chinese Academy of Sciences,Shenyang,110016,China
3.Key Lab for Environmental Computation and Sustainability of Liaoning Province,Shenyang,110016,China
4.Department of Compute Science and Engineering,Southern University of Science and Technology,China

Xiao，C.,Xie，X.,Zhang，L.,et al. Efficient building category classification with fAÇade information from oblique aerial images[C],2020:1309-1313.