共查询到17条相似文献，搜索用时 140 毫秒
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舰船辐射噪声是由舰船上机械运转和舰船运动产生并辐射到水中的噪声,是水下目标探测系统对运动舰船进行探测、识别的基础,亦是扫雷装备需重构的重要物理场信息,具有重要的理论和应用价值。 基于舰船辐射噪声的来源和时频空域特点,将作为体积目标的舰船辐射噪声源简化为 3 个主要噪声辐射源,建立了“三亮点” 的体积目标辐射噪声模型,在保留目标主要特征的基础上,简化了重构算法的复杂性,提高了模型的实用性,可以为实验室目标探测、识别研究和扫雷装备提供较为逼真的目标数据来源。 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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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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随着海上交通运输业业务需求的不断增加,传统的目标检测方法已无法满足实际需求。由于卫星遥感技术的快速发展,基于合成孔径雷达（Synthetic Aperture Radar,SAR）图像舰船目标自动识别具有显著的应用潜力。近年来,深度学习技术在目标检测领域逐渐显现出优势,特别是YOLO (You Only Look Once)模型以其较高的精度和计算效率,为SAR舰船目标的识别提供了一种新的方法。为对比不同的YOLO模型在舰船目标识别领域的性能及其相比于两阶段深度学习算法的优势,本文首先对YOLO系列的结构进行了归纳总结;其次对当前广泛使用的数据集进行了对比分析,并基于SAR图像数据集（SAR Ship Detection Dataset,SSDD）的样本进行重新标注构建出本文的数据集;然后将YOLO系列模型与两阶段目标检测方法——更快速的区域卷积神经网络（Faster Region-based Convolutional Neural Network,Faster R-CNN）在SAR舰船目标检测的精度和速度两方面进行对比实验;最后在YOLOv5模型的基础上对主干网络（Backbone... 相似文献
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给出了一个基于模糊C均值聚类(FCM)的舰船目标检测算法。其中心思想是将SAR图像中的各像素点灰度值视为样本集,然后利用FCM算法对该样本集聚类,并利用聚类结果计算图像分割全局阈值。与目前流行的恒虚警率(CFAR)检测算法相比,所给检测算法具有参数少、计算量与图像大小成正比、舰船轮廓保持良好的特点,为高分辨率SAR图像舰船目标检测提供了一个新的选择。 相似文献
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针对机载合成孔径雷达(SAR)对海探测特点,采用多入射角法从SAR数据本身得到与海浪参数反演区域时空匹配的同步海面风速和风向,并结合线性变换关系,计算得到海浪初猜谱对应的仿真SAR图像谱,将仿真SAR图像谱和观测SAR图像谱输入代价函数中进行迭代运算,通过非线性方程的解算得到最适海浪谱;采用交叉谱法去除海浪传播180°方向模糊,最终得到海浪参数。论文提出的基于同步风场的机载SAR海浪参数反演方法,充分利用了机载SAR海洋环境探测的优势,解决了传统SAR海浪参数反演中初猜谱构造依赖外部风场的问题,机载同步飞行试验的海浪参数反演结果与浮标观测值的有效波高、波向的均方根误差分别为0.23 m和13.23°,验证了该方法的有效性,可为机载SAR海浪参数反演业务化提供支持。 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：1，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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针对现有的轨迹相似度匹配算法用于渔船AIS (Automatic Identification System)与ARPA (Automatic Radar Plotting Aid)轨迹匹配时存在复杂度高、效率低等问题,本文提出基于时空约束和三角形迭代划分的渔船AIS与ARPA轨迹匹配算法TSC-TIP (Temporal and Spatial Constraint-Triangle Iterative Partitioning)。首先采用时空约束法筛选出ARPA目标时空约束范围内的AIS数据;其次采用三角形相似算法选择与AIS数据具有相似特征点的APRA轨迹数据;最后设计了子轨迹迭代划分法将每条轨迹划分为两条子轨迹并采用三角形相似法对子轨迹进行迭代筛选。为验证算法的性能,用渔船的真实AIS轨迹数据和ARPA轨迹数据进行了试验,结果表明：与基于经典距离的相似性度量方法相比,提出的TSC-TIP算法在不影响匹配准确率的前提下,匹配时间减少了95%。研究表明：TSC-TIP算法能有效匹配渔船AIS与ARPA轨迹数据,为面向AIS与ARPA的渔船轨迹数据融合研究提供了新思路。 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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恒虚警(CFAR)检测是地波雷达船只目标探测的主要方法。目前基于船舶自动识别系统(AIS)信息的CFAR检测验证方法是一种间接验证方式,容易受地波雷达系统测向误差的影响,且不具备对错检与漏检目标的分析能力。本文提出了一种基于AIS信息的评价地波雷达CFAR检测结果的直接验证方法。该方法将有效的AIS信息转换到地波雷达的距离-多普勒谱中,通过在该谱中AIS信息与CFAR检测结果的关联分析,实现CFAR检测结果的直接评价。论文首先给出了方法的原理和处理流程,然后利用实测数据给出了该方法在CFAR检测结果评价中的实际应用,验证了方法有效性,而且该方法也为低可观测目标的CFAR检测提供了参数优化调整的依据。 相似文献
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Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training. 相似文献
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A new CFAR ship target detection method in SAR imagery 总被引：8，自引：2，他引：6
Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target pixels, and a binary image is given as the preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use the two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAR imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuming, and is suitable for the operational ship detection system. 相似文献
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A detection model of underwater topography with a series of SAR images acquired at different time 总被引：1，自引：1，他引：0
underwater topography is one of oceanic features detected by Synthetic Aperture Radar. Underwater topography SAR imaging mechanism shows that tidal current is the important factor for underwater topography SAR imaging. Thus under the same wind field condition, SAR images for the same area acquired at different time include different information of the underwater topography. To utilize synchronously SAR images acquired at different time for the underwater topography SAR detection and improve the precision of detection, based on the detection model of underwater topography with single SAR image and the periodicity of tidal current, a detection model of underwater topography with a series of SAR images acquired at different time is developed by combing with tide and tidal current numerical simulation. To testify the feasibility of the presented model, Taiwan Shoal located at the south outlet of Taiwan Strait is selected as study area and three SAR images are used in the underwater topography detection. The detection results are compared with the field observation data of water depth carried out by R/V Dongfanghong 2, and the errors of the detection are compared with those of the single SAR image. All comparisons show that the detection model presented in the paper improves the precision of underwater topography SAR detection, and the presented model is feasible. 相似文献
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ASAR波模式数据反演参数误差与海浪条纹清晰度的相关性分析 总被引：1，自引：0，他引：1
欧洲环境卫星-高级合成孔径雷达(EnvironmentalSatellite-AdvancedSyntheticAperture Radar,Envisat-ASAR)波模式数据提供了全球风、浪要素信息,在海浪模式预报与同化方面有重要作用。该数据合成孔径雷达(SyntheticApertureRadar,SAR)图像普遍存在海浪条纹清晰度不同的现象,但是否影响数据精度尚无定论。本文通过比较2010年NODC (the National Oceanographic Date Center)浮标观测数据和波模式数据,发现经过官方修正后的海浪参数反而具有更大误差。进而通过对比不同条纹清晰度的SAR图像反演参数误差,揭示了ASAR产品海浪参数与浮标测量值之间的误差与海浪条纹清晰度的关系。结果表明:海浪条纹清晰的SAR图像的主波波长和主波周期的反演误差更小,而条纹不清晰SAR图像的有效波高和风速的反演误差更小。通过分析海浪参数对海浪条纹清晰度的敏感性,证实了有效波高和方位向截断波长对SAR图像条纹清晰度的响应最好,波陡次之,与卫星飞行方位角和入射角无关。因此,在反演和修正SAR波模式数据时,考虑图像的条纹清晰度,将会有效提高反演数据的精度。该研究可为高分三号等卫星的波模式数据波浪要素反演精度的提升提供有价值的参考。 相似文献