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Feature reassembly and self-attention for oriented object detection in remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Electronic Information, Northwestern Polytechnical University, Xi'an, 710072, China

  • Email：minlingtong@nwpu.edu.cn
  • Introduction：闵令通，研究方向为人工智能、智能感知和模式识别。E-mail： minlingtong@nwpu.edu.cn
MIN Lingtong1,  
  • Affiliation：

    School of Electronic Information, Northwestern Polytechnical University, Xi'an, 710072, China

FAN Ziman1,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China

XIE Xingxing2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronic Information, Northwestern Polytechnical University, Xi'an, 710072, China

  • Email：lvqinyi@nwpu.edu.cn
  • Introduction：吕勤毅，研究方向为人工智能、雷达信息处理和智能感知。E-mail： lvqinyi@nwpu.edu.cn
LYU Qinyi1*

resumen

Oriented object detection in remote sensing images is an exceptionally challenging task that has elicited widespread attention. With the rapid advancement of deep learning, neural networks based on convolutional neural networks and self-attention networks (e.g., Transformers) have achieved remarkable progress in oriented object detection. However, the focus on boundary and salient feature information in oriented objects in remote sensing images is lacking. Specifically, extracting boundary information for objects with varying orientations is difficult, and the global dependency of salient features is sparse. To address these issues, we propose a method of small-object detection in remote sensing images on the basis of feature reassembly and self-attention. This method consists of a regression branch that incorporates spatial channel reassembly and a self-attention classification branch. The regression branch reassembles spatial information along the channel dimension and emphasizes boundary-sensitive information to achieve accurate localization of bounding boxes. The classification branch leverages self-attention with positional information to capture fundamentally discriminative object features, thus enhancing global feature dependencies for precise classification. Extensive experiments demonstrate the effectiveness and robustness of the proposed model and showcase its excellent performance on publicly available datasets, such as DOTA, HRSC2016, and SODA-A.

palabra clave

remote sensing image;small object detection;detection head;feature reorganization;transformer

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