Movable Object Detection in Remote Sensing Images via Dynamic Automatic Learning

Abstract

The performance of deep networks for object detection in remote sensing images (RSIs) largely depends on the availability of large-scale training images whose labels are given at the bounding-box level through a labor-intensive manual labeling process. To alleviate the huge burden of providing bounding-box annotations manually for movable objects, we propose a new approach, called dynamic automatic learning (DAL), to progressively learn object detectors. Specifically, a novel initial annotation generation (IAG) strategy is first designed to produce bounding-box annotations for movable objects in multi-temporal remote sensing images. During this process, image-level labels need to be manually labeled for the generated candidates. Next, a detection network learns the detection knowledge from multi-temporal remote sensing images with bounding-box annotations and then transfers the knowledge to generate pseudo boxes for the unlabeled data. Finally, with these pseudo boxes, the object detector can be optimized for generating accurate pseudo boxes iteratively. Furthermore, we introduce a pseudo box filtering (PBF) strategy to purify the quality of pseudo boxes to obtain accurate supervision. Our experiments on the challenging NWPU VHR-10.v2 and DIOR datasets have demonstrated that our DAL approach can achieve competitive results compared to state-of-the-art methods.