Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching

摘要

Automatic object detection from remote sensing images has attracted a significant attention due to its importance in both military and civilian fields. However, the low confidence of the candidates restricts the recognition of potential objects, and the unreasonable predicted boxes result in false positives (FPs). To address these issues, an accurate and fast object detection method called the refined single-shot multibox detector (RSSD) is proposed, consisting of a single-shot multibox detector (SSD), a refined network (RefinedNet), and a class-specific spatial template matching (STM) module. In the training stage, fed with augmented samples in diverse variation, the SSD can efficiently extract multiscale features for object classification and location. Meanwhile, RefinedNet is trained with cropped objects from the training set to further enhance the ability to distinguish each class of objects and the background. Class-specific spatial templates are also constructed from the statistics of objects of each class to provide reliable object templates. During the test phase, RefinedNet improves the confidence of potential objects from the predicted results of SSD and suppresses that of the background, which promotes the detection rate. Furthermore, several grotesque candidates are rejected by the well-designed class-specific spatial templates, thus reducing the false alarm rate. These three parts constitute a monolithic architecture, which contributes to the detection accuracy and maintains the speed. Experiments on high-resolution panchromatic (PAN) images of satellites GaoFen-2 and JiLin-1 demonstrate the effectiveness and efficiency of the proposed modules and the whole framework.