DEEP LEARNING MODEL FOR TARGET DETECTION IN REMOTE SENSING IMAGES FUSING MULTILEVEL FEATURES

Target detection in remote sensing image has long been one of the research focuses in related areas. This paper proposed a deep learning model for target detection in remote sensing image fusing multilevel features and applied to detect aircrafts in remote sensing images. Because the model is small in size and applies fusion of multilevel features, the detection accuracy of aircraft targets with different scales and denser in remote sensing images has been improved, without compromising the detection speed. A packet fusion reject detection bounding boxes (PFR-DBB) algorithm was also proposed, which is able to better remove redundant detection boxes and further improve detection accuracy. With the experiment results of two remote sensing aircraft data sets detection based on the model, it is proved that small-scale deep networks can also achieve high performance for multi-scale aircraft target detection on small sample data sets.