Automatic and Fast PCM Generation for Occluded Object Detection in High-Resolution Remote Sensing Images

Partial configuration model (PCM) is an occluded object detection method in high-resolution remote sensing images (HR-RSIs) based on the deformable part-based model (DPM). However, it needs extra category predefinition, considerable part-level annotation, and repeated multimodel training. In this letter, an automatic and fast PCM generation method is proposed based on a novel part sharing mechanism. We propose to share parts from one trained DPM model (tDPM) among different models of partial configurations (PCs) to address the above problems. PCs are first designed according to part anchors of tDPM. The model is then generated through corresponding parts selection, root coverage cropping, and elements reweighing. This method avoids the need for manual category predefinition and part-level annotation, while largely reducing the computation of PCM training. Experimental results on three HR-RSI data sets show that the proposed method obtains a training speedup of 6.7x and 2x for each PC of airplane and ship categories, while achieving a comparable accuracy compared with PCM.