A Gabor Filter-Based Protocol for Automated Image-Based Building Detection

Buildings

Sara Imran Khan¹  Hafiz Suliman Munawar²  Zakria Qadir³  Abbas Z. Kouzani⁴  M. A. Parvez Mahmud⁴  Riya Aggarwal⁵ 

[1] Faculty of Chemical Engineering, University of New South Wales, Kensington, NSW 2052, Australia;School of Built Environment, University of New South Wales, Kensington, NSW 2052, Australia;School of Computing Engineering and Mathematics, Western Sydney University, Penrith, NSW 2751, Australia;School of Engineering, Deakin University, Geelong, VIC 3216, Australia;School of Mathematics and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia;

关键词: building detection;    aerial image dataset;    image processing;    local feature extraction;   

DOI  :  10.3390/buildings11070302

来源: DOAJ

【 摘 要 】

Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively.

【 授权许可】

Unknown