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Recognition of small-target ships in complex satellite images

  • role： First author第一作者
  • Affiliation：

    College of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China

  • Email：yaohongge@xatu.edu.cn
  • Introduction：姚红革，1968年生，男，副教授，研究方向为机器学习和模式识别的理论和应用。E-mail: yaohongge@xatu.edu.cn
YAO Hongge1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China

  • Email：jsycwangc@163.com
  • Introduction：王诚，1993年生，男，硕士研究生，研究方向为深度学习和计算机视觉。E-mail: jsycwangc@163.com
WANG Cheng1*,  
  • Affiliation：

    College of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China

YU Jun1,  
  • Affiliation：

    College of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China

    Key Lab of Electronic Information Processing with Applications in Crime Scene Investigation, Ministry of Public Security, Xi'an 710121, China

BAI Xiaojun13,  
  • Affiliation：

    Technological Center, Xi'an Technological University, Xi'an 710021, China

LI Wei2

résumé

As an important target at sea, the realization of automatic recognition of ships is of great siguificance. The diversity of ship classes and complexity of marine environment exert higher requirements for fine-grained recognition of ships in satellite remote sensing images. Many domestic and foreign experts and scholars have carried out extensive research on ship recognition in remote sensing images. However, no universal method can recognize all kinds of ships. Most of the existing methods have strong specificity; poor portability, that is, these methods can only recognize certain classes of ship targets; and complex processing methods. This work proposes a multiscale deep learning model, which avoids complex modeling methods, such as template matching and background modeling. The network can adaptively determine the network parameters and realize the fine-grained recognition of ships by training large number of samples.First, this work uses negative sample enhancement learning to train the model, and fog and coastal backgrounds are sent as negative samples to the network for training to solve the influence of complex sea conditions, such as cloud–fog occlusion, and coastal background. At the same time, a multiscale sample training method is used in this paper in view of the problem that the size of some targets in the image is small and affects recognition accuracy. The images are compressed into multiple scales and sent to the network for training so that the network can fully learn the features of various ship sizes, thereby solving the difficulty of small target recognition. Second, the pre-trained ZF model is used for feature extraction, and the feature maps are sent to the region proposal network to generate proposal areas. Finally, the generated candidate areas are sent to the fully connected layer for ship fine-grained recognition.Experimental results show that the precision and recall of our method increased by 6.98% and 18.17% respectively, and the accuracy of ship recognition can reach 92.27% compared with Faster R-CNN. The method can guarantee real-time requirement based on high recognition accuracy and recognize ships under various conditions.The trained model can realize the fine-grained recognition of ships. The model not only solves the problems of cloud–fog occlusion to ships, but also the difficulty of small target recognition. The accuracy and real-time performance of the model meet the actual requirements and has strong robustness. However, the experimental results also show that the method used still has shortcomings. First, the constructed network structure has high complexity and excessive network overhead, which increase the processing time of ship target recognition; Secondly, the accuracy of the trained ship recognition model can still be improved. These two points are also the key tasks for the follow-up work.

mots-clés

remote sensing;ship recognition;complex sea conditions;multi-scale;fine-grained;deep learning

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