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Dim target detection from sea clutter is one of the difficult topics in ocean remote sensing application. By aiming at the shortcoming of false alarms when using track before detect (TBD) based on dynamic programming, a new discrimination method called statistics of direction histogram (SDH) is proposed, which is based on different features of trajectories between the true target and false one. Moreover, a new series of discrimination schemes of SDH and Local Extreme Value method (LEV) are studied and applied to simulate the actually measured radar data. The results show that the given discrimination is effective to reduce false alarms during dim targets detection. 相似文献
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船只目标SAR、HFSWR和AIS多手段融合探测的点迹关联分析 总被引：3，自引：1，他引：2
A space-borne synthetic aperture radar （SAR）, a high frequency surface wave radar （HFSWR）, and a ship automatic identification system （AIS） are the main remote sensors for vessel monitoring in a wide range. These three sensors have their own advantages and weaknesses, and they can complement each other in some situations. So it would improve the capability of vessel target detection to use multiple sensors including SAR, HFSWR, and A/S to identify non-cooperative vessel targets from the fusion results. During the fusion process of multiple sensors＇ detection results, point association is one of the key steps, and it can affect the accuracy of the data fusion and the efficiency of a non-cooperative target＇s recognition. This study investigated the point association analyses of vessel target detection under different conditions： space- borne SAR paired with AIS, as well as HFSWR, paired with AIS, and the characteristics of the SAR and the HFSWR and their capability of vessel target detection. Then a point association method of multiple sensors was proposed. Finally, the thresholds selection of key parameters in the points association （including range threshold, radial velocity threshold, and azimuth threshold） were investigated, and their influences on final association results were analyzed. 相似文献
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全极化SAR图像中溢油极化特征研究 总被引：2，自引：1，他引：1
相比于单极化SAR图像,全极化SAR图像不仅能体现海面目标的几何特征、后向散射特征,还能体现目标的极化特征。因此,在溢油检测方面,极化SAR更具优势。特征提取作为溢油检测的关键步骤,直接影响到溢油检测的精度。在本文中,我们分析了全极化SAR图像中海面溢油的极化特征,如极化散射熵、平均散射角等。并提出了新的极化特征P,该特征参数能够反映海面目标电磁散射过程中布拉格散射机制和镜面散射机制的比例。为了研究极化特征溢油检测的能力,本文基于SIR-C/X-SAR和Radarsat-2全极化SAR图像开展了相关实验,并对比分析了溢油的多种极化特征。实验结果显示,在中低风速情况下,C波段溢油探测效果优于L波段;本文提出的极化特征P对海面散射机制敏感;基准高度和特征参数P在C波段比其他极化特征更适于溢油检测。 相似文献
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全极化合成孔径雷达(Synthetic Aperture Radar,SAR)数据具有丰富的极化信息,能够提取出大量异构性特征。核学习方法在解决小样本、高维特征分类问题上具有优势,但异构特征对不同核函数具有响应差异。本文利用一种引入先验标签的多核学习方法进行全极化SAR的溢油信息提取,即基于分析结果对特征集进行遴选与组合,分别在每个特征组合中训练得到一个预备层核函数,以新获取的预备层核函数作为新的底层核函数,对全部特征进行学习分类。通过提取与分析溢油和海水的统计特征、物理散射特征和纹理特征,建立溢油全极化SAR特征谱,并利用引入先验标签的多核学习分类器进行溢油提取实验。结果表明,该方法能够利用全极化SAR多维异构特征的互补特性有效提高溢油分类提取精度。 相似文献
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由于光照的变化和全景视觉的畸变,全自主足球机器人对目标识别不稳定且有效性差,为此提出了1种基于Gabor滤波器和支持向量机(SVM)的全自主足球机器人目标识别方法。首先根据颜色特征和面积、长宽比等简单形状特征提取候选目标,候选目标中包含实际的目标和与目标颜色相近且通过上述简单形状特征判断仍无法消除的干扰。将候选目标与Gabor滤波器作卷积来提取特征向量,将特征向量输入SVM进行分类,识别出实际的目标。在机器人MT-R的足球目标识别中对该方法进行了实验,实验表明该种方法具有较好的识别精度,且满足足球机器人的实时性要求。 相似文献
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在SAR与AIS联合探测探测舰船目标实验中,通过对2010-11-05T06：51渤海海峡Radarsat-2影像的和与之时空匹配的AIS数据比较,发现2种手段探测到的静止的舰船目标能够精确的匹配,但是运动的舰船目标存在一定的偏差.分析了偏差产生的原因,其中距离向偏差是由时间误差引起的;方位向偏差主要是由多普勒频移造成的;这与运动舰船目标的航速、航向及SAR与AIS的探测时间误差有关.在此基础上建立了数学模型,分析上述参数对偏差的影响,并对偏差加以修正,从而实现了SAR与AIS探测结果的匹配. 相似文献
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近年来,海战场成为现代战争的主要作战区域之一,舰船目标逐渐成为海上重点监测对象,能否快速准确地识别海战场舰船目标的战术意图,给指挥员的决策提供必要的支持,这关系到一场海上战役的成败.随着合成孔径雷达(synthetic aperture radar,SAR)成像技术的不断发展,大量SAR图像可用于舰船目标检测与识别.利用SAR图像进行舰船目标检测与识别,已经成为重要的海洋应用之一.针对传统SAR图像舰船检测方法准确率较低的问题,本文在YOLOv3的基础上,结合感受野(receptive field block,RFB)模块,提出一种增强型的SAR舰船检测方法.该方法在最近公开的SAR图像舰船检测数据集上平均准确率值达到了91.50％,与原YOLOv3相比提高了0.92％.实验结果充分表明本文提出的算法在SAR舰船的检测中具有较好的检测效果. 相似文献
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基于合成孔径雷达交叉极化通道数据的海上目标探测 总被引：1，自引：0，他引：1
Azimuth ambiguities(ghost targets) discrimination is of great interest with the development of a synthetic aperture radar(SAR). And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ± for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e.(S S)HV VH, hereafter called A12 r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12 r image between a target and the surrounding sea surface will be obviously increased when A12 r image is processed by mean filtering algorithm. Here, in order to detect target with constant false-alarm rates(CFARs), an analytical expression for the probability density function(pdf) of A12 r is derived based on the complex Wishart-distribution. Because a value of A12 r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity. 相似文献
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合成孔径雷达在海洋环境监测和海洋研究中扮演着越来越重要的角色。受其成像机制的影响，合成孔径雷达图像总是受到斑点噪声的污染。斑点噪声的存在会增大目标识别、跟踪和分类的难度，也会降低雷达信号的信噪比。合成孔径雷达海洋图像具有一些特殊的性质：海洋现象在雷达图像中主要呈现为条带状或斑块状的结构。这些条带状或斑块状的结构呈现出高度的自相似性或信息冗余。非局部平均方法能够衡量图像中不同图像块之间纹理结构的相似性，并利用图像的自相似性对图像进行去噪。但非局部平均去燥方法存在计算量巨大、计算耗时长的缺点，这几乎限制了其实际应用。本文采用一种自适应方法将雷达图像中的像素点区分为纹理区像素点和平坦区像素点。对纹理区像素点，采用较大的相似窗和搜索窗，对平坦区像素点，采用较小的相似窗和搜索窗，从而提高计算速度。进一步，本文基于计算统一设备并行架构（CUDA）技术，利用计算机图形处理器（GPU）对前述算法进行并行加速。与经典非局部平均算法相比，加速后算法的计算效率提高了200倍。 相似文献
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灰度共生矩阵纹理特征对SAR海冰漂移监测的增强性能研究 总被引：1，自引：0，他引：1
海冰漂移监测对气候变化分析、船只航行、海上石油平台等海上活动安全作业具有重要意义。当前主流的SAR海冰漂移监测方法多是基于SAR灰度图开展的,其受噪声、环境等因素的影响较大,导致其在海冰漂移探测时,特征失配率高,匹配正确率低。针对这一问题,本文尝试利用SAR海冰纹理特征来增强海冰漂移探测性能。首先对比分析了8种纹理特征对海冰漂移探测中特征匹配的增强性能,筛选出能够有效增强特征匹配性能的最优纹理特征;其次进一步分析了海冰类型、入射角和分辨率对基于纹理特征的海冰漂移探测性能增强的影响。实验结果表明,均值是最优的纹理特征,与SAR强度图相比,特征匹配正确率提高了约7%。 相似文献
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The effectiveness of 2 methods for targeting observations is examined using a T21 L3 QG model in a perfect model context. Target gridpoints are chosen using the pseudo‐inverse (the inverse composed of the first three singular vectors only) and the quasi‐inverse or backward integration (running the tangent equations with a negative time‐step). The effectiveness of a target is measured by setting the analysis error to zero in a region surrounding the target and noting the impact on the forecast error in the verification region. In a post‐time setting, when the targets are based on forecast errors that are known exactly, both methods provide targets that are significantly better than targets chosen at random within a broad region upstream of the verification region. When uncertainty is added to the verifying analysis such that the forecast error is known inexactly, the pseudo‐inverse targets still perform very well, while the backward integration targets are degraded. This degradation due to forecast uncertainty is especially significant when the targets are a function of height as well as horizontal position. When an ensemble‐forecast difference is used in place of the inexact forecast error, the backward integration targets may be improved considerably. However, this significant improvement depends on the characteristics of the initial‐time ensemble perturbation. Pseudo‐inverse targets based on ensemble forecast differences are comparable to pseudo‐inverse targets based on exact forecast errors. Targets based on the largest analysis error are also found to be considerably more effective than random targets. The collocation of the backward integration and pseudo‐inverse targets appears to be a good indicator of target skill. 相似文献
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溢油事件的发生会给海洋环境的保护和经济发展带来巨大的影响。运用现代化的监测手段和技术进行监测,及时发现溢油现象和违规行为,保护海洋环境是非常重要的。合成孔径雷达（SAR）技术是溢油检测的有效工具,在SAR图像中溢油表现为黑色的区域,但是黑色区域也可能会由其他的因素引起。本文提出了一种基于二维经验模态分解（BEMD）的方法来识别溢油和疑似溢油。首先通过BEMD方法将感兴趣的区域分解为局部窄带的各分量—内蕴模函数（BIMF）之和,并对分解后得到的各分量IMF进行Hilbert变换,通过Hibert谱分析得到64维的特征空间,然后使用Relief方法得到5个特征向量,最后利用马氏距离分类器进行分类。通过实验结果表明,该方法能够有效、准确地检测出溢油,准确率超过90%。 相似文献
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Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization. 相似文献
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高频地波雷达海上目标航迹跟踪新思路 总被引：1，自引：1，他引：0
高频地波雷达是对海上运动目标进行监视监测的一种重要手段,为了提高地波雷达对海上特定目标独立跟踪探测时的性能,本文对高频地波雷达海上目标跟踪技术的研究现状进行了综述,分析总结了目前航迹跟踪方法存在的主要问题。结合海上目标跟踪的实际应用需求,借助目前流行的深度学习方法充分挖掘其他同步探测手段获取的目标信息,提出了基于知识辅助的特定目标跟踪方法,改善后续地波雷达对特定目标独立跟踪时的航迹质量,初步的航迹跟踪结果验证了提出方法的有效性。提出的地波雷达特定目标跟踪方法对目标跟踪方法的理论研究及地波雷达目标跟踪系统的业务化应用均具有重要意义及参考价值。 相似文献
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The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities. 相似文献
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Dura E. Yan Zhang Xuejun Liao Dobeck G.J. Carin L. 《Oceanic Engineering, IEEE Journal of》2005,30(2):360-371
A data-adaptive algorithm is presented for the selection of the basis functions and training data used in classifier design with application to sensing mine-like targets with a side-scan sonar. Automatic detection of mine-like targets using side-scan sonar imagery is complicated by the variability of the target, clutter, and background signatures. Specifically, the strong dependence of the data on environmental conditions vitiates the assumption that one may perform a priori algorithm training using separate side-scan sonar data collected previously. In this paper, a novel active-learning algorithm is developed based on kernel classifiers with the goal of enhancing detection/classification of mines without requiring an a priori training set. It is assumed that divers and/or unmanned underwater vehicles (UUVs) may be used to determine the binary labels (target/clutter) of a small number of signatures from a given side-scan collection. These sets of signatures and associated labels are then used to train a kernel-based algorithm with which the remaining side-scan signatures are classified. Information-theoretic concepts are used to adaptively construct the form of the kernel classifier and to determine which signatures and associated labels would be most informative in the context of algorithm training. Using measured side-looking sonar data, the authors demonstrate that the number of signatures for which labels are required (via diver/UUV) is often small relative to the total number of potential targets in a given image. This procedure designs the detection/classification algorithm on the observed data itself without requiring a priori training data and also allows adaptation as environmental conditions change. 相似文献
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合成孔径雷达(SAR)图像的斑点噪声严重阻碍了其数据的解译和应用,从数学物理的观点描述SAR图像的衰落统计特性,并将仿真结果与真实SAR图像作比较,得出两者具有一致性,最后提出根据SAR图像衰落统计特性的目标检测方法。 相似文献