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Complex cue visual attention model for Harbor detection in high-resolution remote sensing images

  • role： First author第一作者
  • Affiliation：

    Naval University of Engineering, Department of Electronic Engineering, Wuhan 430033, China

  • Email：MaoLing_1980@163.com
  • Introduction：毛玲(1980—　)，女，讲师，研究方向为模式识别、智能信号处理。E-mail：MaoLing_1980@163.com
MAO Ling1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Naval University of Engineering, Department of Equipment Economics and Management, Wuhan 430033, China

  • Email：Guomin_Zhang@163.com
  • Introduction：张国敏(1980—　)，男，讲师，研究方向为人工智能、模式识别。E-mail：Guomin_Zhang@163.com
ZHANG Guomin2*

resumen

Harbor detection in high-resolution remote sensing images is a complex scene object detection problem, which is significant for applications such as harbor monitoring and space reconnaissance. This process is commonly requires complex feature extraction and analysis with high computational complexity. Existing works often select a smaller feature extraction region utilizing a scenario set according to experience and intuition and deal with a trade-off between computational complexity and object detection accuracy. The resolution and extraction order of features must be considered systematically to resolve this dilemma.This paper reports on a complex cue visual attention model, which is a bionic human visual attention mechanism, for harbor detection in high-resolution remote sensing images. It combines multi-scale, low-level features with high-level knowledge clues in proper resolution layers of the Gauss pyramid of the input image. This model also performs natural feature integration and comprehensive classification reasoning for harbor detection. Multi-step fast algorithms are employed to lower the computational complexity of the entire algorithm.Experiments in high-resolution optical remote sensing images from different satellites validate the proposed method. Non-harbor regions are immediately excluded from complicated feature extraction, whereas regions that most likely contain harbors are focused on for feature extraction and analysis. Thus, harbor regions are rapidly located and detected under the condition of limited computational resources. Detection effects are improved with complex feature extraction and analysis with a minimal increase in computational complexity.The complex cue visual attention model proposed in this study considers the resolution and extraction order of features systematically. It lowers the computational complexity of traditional object detection methods largely without decreasing the detection accuracy and can be applied in other complex scene object detection problems.

palabra clave

visual attention;high resolution;remote sensing image;harbor detection;complex cues

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