Objects Localization in Remote Sensing Images Using Local Features Clustering - Farid, Muhammad; Badran, Khaled; Elnashar, Gamal (International Journal For Research In Electronics & Electrical Engineering, 2016)
There is an increasing trend towards object detection from aerial and satellite images. Most of the recent state-of the-art widely used object detection researches based on local features use the scanning of images by the sliding window. In this paper we propose an approach to localize the candidate objects by using the clustering of locations of the matched keypoints, this method has a benefits of minimizing the no of points to be processed by the classifier, and with more accuracy. In this paper, this approach is tested by SIFT and SURF local features detector and descriptor. This approach can be used as an object detection technique by itself or executed as a pre-step before apply the machine learning trained classifiers to achieve more precise results.