Target heat-map network: An end-to-end deep network for target detection in remote sensing images

摘要

In this paper, we focus on approaching fast and accurate target detection in high-resolution remote sensing images (RSIs). Recently, machine-learning based target detection systems have drawn increasing attention and some excellent target detection frameworks have been proposed for RSIs. However, huge storage and time consumption are still key flaws of those methods in practical applications. Especially, the employment of transfer learning would produce tremendous growth in parameters. A new target detection framework named target heat-map network (THNet) is proposed in this paper to address these problems. This framework consists of three parts: shallow features-extracting network, decoder network, and the location method. Firstly, we introduce the transfer-compression learning to train a shallow network under the supervision of a deep pre-trained network. Secondly, a decoder network is constructed to predict heat-map layers. Finally, a location method based on thresholding is proposed to identify positions of target instances. Compared with existing state-of-the-art methods including Faster-R-CNN, YOLOv2 and SSD, THNet with transfer learning has better performance, and THNet with transfer-compression learning also has superior performance in quantitative evaluation as well as significantly reducing time and saving storage, which makes it a great choice for applications with critical requirements for storage cost and running time. (C) 2018 Elsevier B.V. All rights reserved.