Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets

Haijiang Sun, Qiaoyuan Liu, Jiacheng Wang, Jinchang Ren, Yanfeng Wu, Huimin Zhao, Huakang Li

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Abstract

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach.

Original languageEnglish
Pages (from-to)2971-2983
Number of pages13
Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume14
DOIs
Publication statusPublished - 24 Feb 2021

Keywords

  • background subtraction
  • image fusion
  • LSS Target detection
  • saliency detection

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