Local feature based supervised object detection: Sampling, learning and detection strategies

In this paper, we investigate different architectures for an efficient object detection processing chain for high resolution remote sensing imagery, inspired from work in natural images where object detection has reached an almost operational state. Such a processing chain consists of several tasks, and for each of them, one or more methods are proposed in this paper: examples database, negative examples sampling, relevant features, learning and detection strategies, etc. Experimental results are presented, showing that the histogram of oriented gradient descriptor seems to be the most appropriate one for plane detection at a resolution of 70 centimeters.