@article{MAKHILLJEAS2017121714793,
    title = {Vehicle Detection on Images from Satellite using Oriented Fast and Rotated Brief},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {17},
    pages = {4500-4503},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.4500.4503},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.4500.4503},
    author = {Joko Lianto,Chastine,Darlis,Diagnosa,Hera and},
    keywords = {Satellite images,vehicle detection,feature extraction,recall,MSER,classification},
    abstract = {Traffic density data plays important role in traffic management, road planning as well as urban land
use planning. Several efforts have been used to gather this data, mainly by detecting and counting vehicles
in roads by processing images from CCTV placed in certain positions in roads. The main disadvantage of this
approach is that it is only possible to detect and count vehicles effectively in a relatively limited area of the
roads due to limited height and camera resolution. By using satellite images or images taken from drones, the
coverage area of the roads can be increased significantly, however problems of false detection due to objects
looking similar to vehicles also increases. This reseach uses Template Matching methos by using correlation
equation, haar cascade classification, keypoint detection using maximally stable extremal region and Oriented
FAST and Rotated BRIEF (ORB) feature extraction method. The highest recall and precision value using MSER
and ORB are 100 and 75%, respectively.}
    }