系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (12): 3667-3675.doi: 10.12305/j.issn.1001-506X.2022.12.10

• 传感器与信号处理 • 上一篇    

基于轻小型无人机雷达的植被高度反演方法

吴志鹏1,2,3, 张平1,2,*, 李震1,2, 黄磊1,2, 刘畅1,2,3, 高硕1,2,3   

  1. 1. 中国科学院空天信息创新研究院, 北京 100094
    2. 三亚中科遥感研究所 海南省地球观测重点实验室, 海南 三亚 572029
    3. 中国科学院大学, 北京 100049
  • 收稿日期:2021-06-21 出版日期:2022-11-14 发布日期:2022-11-24
  • 通讯作者: 张平
  • 作者简介:吴志鹏(1996—), 男, 硕士研究生, 主要研究方向为信号处理、雷达遥感植被参数反演|张平(1979—), 女, 高级工程师, 博士, 主要研究方向为合成孔径雷达信号处理、超分辨率图像处理、极化定标|李震(1966—), 男, 研究员, 博士, 主要研究方向为地物目标散射机制、SAR地表参数反演、冰冻圈环境变化|黄磊(1982—), 男, 副研究员, 博士, 主要研究方向为微波遥感、极化散射机制|刘畅(1995—), 女, 博士研究生, 主要研究方向为积雪产品评估及应用、微波遥感积雪参数反演|高硕(1993—), 男, 博士研究生, 主要研究方向为积雪散射模型、被动微波积雪参数反演
  • 基金资助:
    海南省财政科技计划-海南省重点研发计划(ZDYF2019002);中国科学院空天信息创新研究院重点部署项目(Y950930Z2F)

Vegetation height inversion method based on light-weighted and small UAV-radar

Zhipeng WU1,2,3, Ping ZHANG1,2,*, Zhen LI1,2, Lei HUANG1,2, Chang LIU1,2,3, Shuo GAO1,2,3   

  1. 1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    2. Key Laboratory of Earth Observation of Hainan Province, Sanya Institute of Remote Sensing, Sanya 572029, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-06-21 Online:2022-11-14 Published:2022-11-24
  • Contact: Ping ZHANG

摘要:

针对目前主流雷达传感器难以满足轻小型无人机载荷需要, 且存在着数据获取成本高及算法模型复杂的问题, 在自行研制的一种高度集成、轻量化且具有高可靠性的轻小型无人机雷达系统基础上, 发展了一种植被高度信息反演新方法。该方法中使用二维滤波抑制直耦波能量, 基于剩余图像熵的自适应主元分析去噪方法解决传统方法主元选择不稳定的问题, 并基于互相关信息的后向投影算法进一步增强目标信号, 最后应用Sobel算子提取植被高度。实验结果表明, 该方法反演得到的植被高度和验证数据的相关性达到0.92, 均方根误差可达1.28 m。

关键词: 轻小型无人机, 无人机雷达, 模块化雷达系统, 植被高度, 反演

Abstract:

At present, the mainstream radar sensors are difficult to meet the demands of the light-weighted and small unmanned aerial vehicle (UAV), which usually need high cost of data acquisition and complicate models of algorithm. A highly integrated, lightweight and reliable radar system is designed, which can be carried on the light-weighted and small UAV. Based on this system, a novel method has been developed for vegetation height information inversion. The inversion algorithm suppress the direct coupled wave by two-dimensional filtering, settle the unstable selection of principle component by the adaptive principle component analysis denoising method with residual image entropy, further enhance the target signal by the back-projection algorithm based on cross-correlation information, and finally extract the vegetation height by implementing Sobel operator. The experiment results show that the correlation between the vegetation height derived from the proposed method and the verified data is 0.92, and the root mean square error is 1.28 m.

Key words: light-weighted and small unmanned aerial vehicle (UAV), UAV-radar, modular radar system, vegetation height, inversion

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