An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images

摘要

Faster Region based convolutional neural networks (FRCN) has shown great success in object detection in recent years. However, its performance will degrade on densely packed objects in real remote sensing applications. To address this problem, an enhanced deep CNN based method is developed in this paper. Following the common pipeline of “CNN feature extraction + region proposal + Region classification”, our method is primarily based on the latest Residual Networks (ResNets) and consists of two sub-networks: an object proposal network and an object detection network. For detecting densely packed objects, the output of multi-scale layers are combined together to enhance the resolution of the feature maps. Our method is trained on the VHR-10 data set with limited samples and successfully tested on large-scale google earth images, such as aircraft boneyard or tank farm, containing a substantial number of densely packed objects.