Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model

Deformable-part-based model (DPM) has shown great success in object detection in recent years. However, its performance will degrade on partially occluded objects and is even worse on largely occluded objects in real remote sensing applications. To address this problem, a novel partial configuration object model (PCM) is developed in this paper. Compared to conventional single-layer DPMs, an extra partial configuration layer, which is composed of partial configurations defined according to possible occlusion patterns, is introduced in PCM to block the transmission of occlusion impact. During detection, each hypothesis from a partial configuration layer will infer the entire object based on spatial interrelationship and final detection results are obtained from the fusion of these possible entire objects using a weighted continuous clustering method. As PCM makes a better compromise between the deformation modeling flexibility of small parts and the discriminative shape-capturing capability of large DPM, its performance on occluded object detection will be improved. Moreover, occlusion states of detected objects can be inferred with the intermediate results of our model. Experimental results on multiple high-resolution remote sensing image datasets demonstrate the effectiveness of the proposed model.