{"title":"Low-Frequency Ultrawideband Synthetic Aperture Radar Foliage-Concealed Target Change Detection Strategy Based on Image Stacks","authors":"Hongtu Xie;Shiliang Yi;Jinfeng He;Yuanjie Zhang;Zheng Lu;Nannan Zhu","doi":"10.1109/JSTARS.2024.3477602","DOIUrl":null,"url":null,"abstract":"Low-frequency ultrawideband synthetic aperture radar (UWB SAR) has high-resolution imaging and foliage-penetrating ability, which can detect the foliage-concealed target. However, due to the jungle detection environment and the low-frequency UWB SAR characteristics, there are often some nontarget strong scattering points in low-frequency UWB SAR images, which may increase the difficulty of foliage-concealed target change detection. To improve the change detection rate of the foliage-concealed target, a foliage-concealed target change detection strategy based on image stacks in low-frequency UWB SAR images is proposed. In image preprocessing, a relative radiometric correction method based on the bidirectional linear regression model is presented, which can eliminate the low-frequency UWB SAR image changes caused by nontarget factors. Besides, in change detection processing, multiple difference images are first obtained by subtracting the image to be detected from multiple reference images. Then, the Gaussian probability density function is used to model the distribution of the amplitude of these difference images. Finally, the generalized likelihood ratio test is used for target change detection, which effectively suppresses the interference, such as tree trunk clutter. Experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed strategy, which can improve the change detection probability of the foliage-concealed target with the lower false alarm rate.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"19817-19830"},"PeriodicalIF":4.7000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713238","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10713238/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Low-frequency ultrawideband synthetic aperture radar (UWB SAR) has high-resolution imaging and foliage-penetrating ability, which can detect the foliage-concealed target. However, due to the jungle detection environment and the low-frequency UWB SAR characteristics, there are often some nontarget strong scattering points in low-frequency UWB SAR images, which may increase the difficulty of foliage-concealed target change detection. To improve the change detection rate of the foliage-concealed target, a foliage-concealed target change detection strategy based on image stacks in low-frequency UWB SAR images is proposed. In image preprocessing, a relative radiometric correction method based on the bidirectional linear regression model is presented, which can eliminate the low-frequency UWB SAR image changes caused by nontarget factors. Besides, in change detection processing, multiple difference images are first obtained by subtracting the image to be detected from multiple reference images. Then, the Gaussian probability density function is used to model the distribution of the amplitude of these difference images. Finally, the generalized likelihood ratio test is used for target change detection, which effectively suppresses the interference, such as tree trunk clutter. Experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed strategy, which can improve the change detection probability of the foliage-concealed target with the lower false alarm rate.
低频超宽带合成孔径雷达（UWB SAR）具有高分辨率成像和穿透落叶能力，可以探测到隐藏在落叶中的目标。然而，由于丛林探测环境和低频 UWB SAR 特性，低频 UWB SAR 图像中往往存在一些非目标强散射点，这可能会增加叶面隐蔽目标变化探测的难度。为了提高叶面隐藏目标的变化检测率，本文提出了一种基于低频 UWB SAR 图像中图像堆栈的叶面隐藏目标变化检测策略。在图像预处理中，提出了一种基于双向线性回归模型的相对辐射校正方法，该方法可以消除由非目标因素引起的低频 UWB SAR 图像变化。此外，在变化检测处理中，首先要从多幅参考图像中减去待检测图像，得到多幅差分图像。然后，使用高斯概率密度函数对这些差分图像的振幅分布进行建模。最后，利用广义似然比检验进行目标变化检测，从而有效抑制树干杂波等干扰。在 CARABAS-II SAR 数据集上测试的实验结果表明了所提策略的正确性和有效性，它可以提高树叶遮挡目标的变化检测概率，并降低误报率。
期刊介绍：
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
