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Multiscale object detection in remote sensing image by combining data fusion and feature selection

  • role： First author第一作者
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email：qindengda@csu.edu.cn
  • Introduction：秦登达，研究方向为遥感影像目标检测。E-mail： qindengda@csu.edu.cn
QIN Dengda,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

WAN Li,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

HE Peien,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

ZHANG Yi,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

GUO Ya,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email：cj2011@csu.edu.cn
  • Introduction：陈杰，研究方向为遥感影像智能分析与理解。E-mail： cj2011@csu.edu.cn
CHEN Jie*

résumé

Remote sensing image object detection based on depth neural network model has achieved great success largely due to the support of large-scale data sets. However, from the perspective of the existing remote sensing image datasets, the number distribution of different types of ground objects is inconsistent, and the same type of ground objects is presented in different sizes. This factor leads to the scale imbalance of ground object samples.To alleviate this problem, the strategy of weighted image fusion and multiscale feature selection is adopted. First, the pixel values of the two images in the data set are weighed to obtain the fused image. Therefore, the different types of feature samples are more balanced and have higher background diversity. Second, the target category of the corresponding scale is predicted by selecting the appropriate scale feature map, and the same scale target can be predicted on the adjacent feature map. Thus, the model can be trained according to the target scale. Finally, the bounding box of the target is predicted based on the feature map of the target center area, and the result is more consistent with the scale of the target itself.The image fusion and multiscale feature selection network is experimentally compared in the paper on two datasets, RSOD and NWPUVHR10. The results show that the trained model is more accurate in the recognition of imbalanced objects in complex background and can adapt to the recognition of different scale objects in remote sensing images. In addition, the proposed method enhances the diversity of sample scenes and can be adapted to different scales of targets. Qualitative analysis shows that the proposed method has better robustness and performance advantages when applied to remote sensing images.The proposed method can cope with remote sensing images with complex backgrounds, mitigate the effects of category imbalances and better fit the scenarios in which remote sensing images are used. Mitigation of category imbalances and Scale Selection enhance the performance of remote sensing image object detection.

mots-clés

image fusion enhancement;multi scale selection and expression;high resolution remote sensing image;object detection;convolutional neural network

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