Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration

Existing segmentation methods require manual interventions to optimally extract objects from cluttered background, so that they can hardly work well in automated surveillance systems. In order to automatically extract aircrafts from very high-resolution images, we proposed a segmentation method that combines bottom-up and top-down cues. Three essential principles from local contrast, global contrast, and center bias are involved to compute bottom-up cue. In addition, top-down cue is computed by incorporating aircraft shape priors, and it is achieved by training a classifier from a rich set of visual features. Iterative operations and adaptive fitting are designed to get refined results. Experimental results demonstrated that the proposed method can provide significant improvements on the segmentation accuracy.