Robust geospatial object detection based on pre-trained faster R-CNN framework for high spatial resolution imagery

Geospatial object detection from high spatial resolution (HSR) imagery is significant and challenging for further analyzing the object-related information in various civil and military applications. Traditional object detection methods based on the handcrafted features are limited by their efficiency in describing the multi-class objects from large-swath and complex-context HSR imagery. Although convolutional neural network (CNN) can extract the features automatically, the feature extraction and detection stages are still separate and time-consuming. In addition, manual labelling information is limited and an efficient real-time one-stage detection framework for HSR imagery is scare. In this paper, a robust pre-trained efficient multi-class geospatial object detection framework - pre-trained Faster R-CNN sharing the convolutional features between region proposal stage and detection stage is proposed for HSR imagery. Extensive experiments and evaluations on a ten-class object detection dataset are conducted for the proposed method.