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BMVT-M based IR and SAR ground target detection

机译：基于BMVT-M的红外和SAR地面目标检测

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摘要

IR Target detection is one of the key technologies in military applications. However, IR sensor has limitations of passive sensor such as low detection capability to weather and atmospheric effects. In recent years, sensor fusion is active research topic to overcome the limitations. Additional active SAR sensor is selected for sensor fusion because SAR sensor is robust to various weather conditions. The state-of-the-art detector, BMVT, has good performance in clear environment such as sky and sea background for small target. However, it shows poor performance when the target has extended size or the target is located in complex background such as ground-background with dense clutters. Therefore, we presents an improved ground target detection method based on the BMVT and Morphology filter (BMVT-M). The proposed algorithm consists of two parts: The first part is target enhancement based on the BMVT. The second part is clutter rejection and target enhancement based on the Morphology filter. In addition, conventional BMVT is not suitable to SAR image for target detection because SAR image has many shot noises. Therefore we apply a median filter before the BMVT in SAR image to suppress the shot noise. For the verification of the performance, experiments are performed in various cluttered backgrounds, such as ground, sea, and sky generated by the OKTAL-SE tool. The proposed algorithm showed upgraded detection performance than the BMVT in terms of detection rate and false alarm rate. Moreover, we discuss the applicability of the proposed method to the SAR and IR sensor fusion research.
机译：红外目标检测是军事应用中的关键技术之一。然而，IR传感器具有无源传感器的局限性，例如对天气和大气影响的低检测能力。近年来，传感器融合是克服这些局限性的积极研究课题。选择附加的有源SAR传感器进行传感器融合，因为SAR传感器对各种天气条件都非常坚固。最先进的探测器BMVT在晴朗的环境（例如小目标的天空和大海背景）中具有良好的性能。但是，当目标尺寸扩大或目标位于复杂背景（例如杂波密集的地面背景）中时，其性能会很差。因此，我们提出了一种基于BMVT和形态学滤波器（BMVT-M）的改进的地面目标检测方法。该算法包括两部分：第一部分是基于BMVT的目标增强。第二部分是基于形态滤波器的杂波抑制和目标增强。此外，传统的BMVT不适合用于目标检测的SAR图像，因为SAR图像具有许多散粒噪声。因此，我们在SAR图像的BMVT之前应用中值滤波器以抑制散粒噪声。为了验证性能，在OKTAL-SE工具生成的各种杂乱背景（例如地面，海洋和天空）中进行了实验。提出的算法在检测率和误报率方面均比BMVT提升了检测性能。此外，我们讨论了该方法在SAR和红外传感器融合研究中的适用性。

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  • 来源
    《Automatic Target Recognition XXV》|2015年|947617.1-947617.12|共12页
  • 会议地点 Baltimore MD(US)
  • 作者

    Yunji Lim; Sungho Kim; Yeongrae Jo; Woo Jin Song; Sohyeon Kim;

  • 作者单位

    Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro Gyeongsan-si Gyeongsangbuk-do, 712-749, Korea;

    Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro Gyeongsan-si Gyeongsangbuk-do, 712-749, Korea;

    POSTECH, San 33, Hyoja-dong, Nam-gu, Pohang-si, Gyeongsangbuk-do, Korea;

    POSTECH, San 33, Hyoja-dong, Nam-gu, Pohang-si, Gyeongsangbuk-do, Korea;

    Agency for Defense Development, 111, Sunam-dong, Yuseong-gu, Daejeon, Korea;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Target detection; Sensor fusion; IR; SAR; Boolean map theory; OKTAL-SE;

    机译：目标检测；传感器融合； IR； SAR；布尔映射理论；奥克塔尔;

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