An Effective Zoom-In Approach for Detecting DIM and Small Target Proposals in Satellite Imagery

摘要

Satellite high definition videos provide an opportunity to monitor moving targets over a large territory. However, the low spatial resolution and low contract of these videos make target detecting and tracking a challenging task. In this paper, we propose a zoom-in approach for detecting dim and small target proposals from each single frame of the videos to help with moving target tracking. Initialized by a coarse scale segmentation approach, dim and small targets are embedded in each superpixel due to limited size and weak signals. Similar superpixels are then merged using a graph-based approach based on the measurement of the overlap between their histograms. The background statistics become stronger and target pixels are more obvious in the merged superpixels, so that the target pixels can be extracted. Finally, the corresponding boundary box is generated for each spatially connected target pixels selected inside each superpixel. They form the dim and small target proposals. Experimental results show that our zoom-in scheme can generate less proposals with higher recall rate compared with state-of-the-art proposal extraction algorithms.