共查询到18条相似文献,搜索用时 109 毫秒
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针对侧扫声呐图像斑点噪声强、背景海底散射干扰严重,海底目标轮廓自动提取困难的问题,提出了一种基于K-means聚类与数学形态学相结合的海底目标轮廓自动提取算法。为克服噪声干扰,该算法首先利用中值滤波去除侧扫声呐图像中的强斑点噪声;然后采用K-means聚类算法对侧扫声呐灰度图像进行分割,并二值化,除去大部分海底背景噪声,初步提取出目标;接着利用数学形态学运算去除提取结果中的孤立噪点,并填充目标内部孔洞,得到连续化、圆滑的目标边缘;最后对处理后的侧扫声呐图像进行边缘检测,提取出目标轮廓。实验结果表明:该算法思想简单易行,具有很强的克服背景噪声的能力,自动提取的目标轮廓连续性较好,结果准确可靠。目前,在侧扫声呐图像目标轮廓提取过程中,主要采用人工方式,自动性较差,效率较低。本文算法可以实现目标轮廓的自动提取,提高效率,具有较强的实用价值。 相似文献
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基于数学形态学的侧扫声呐图像轮廓自动提取 总被引:1,自引:0,他引:1
侧扫声呐图像特征自动提取的难点在于特征地貌边缘检测较困难,依据图像灰度突变检测得到的边缘比较粗糙、不连续,而且有断口和小洞。本文在对图像进行预处理和阈值化的基础上,采用数学形态学方法对图像进行处理,即用具有一定形态的结构元素去量度和提取图像中的对应形状,得到连续化、粗化、圆滑的特征区域边缘填充目标内部阴影且消除背景噪声。基于数学形态学的侧扫声呐图像特征自动提取的主要步骤为:首先对侧扫声呐图像进行预处理,然后进行灰度阈值化,接着采用数学形态学方法进行处理,最后对处理后的图像进行边缘检测,提取出特征地貌边缘。实验表明,采用数学形态学方法进行处理后,错断、离散的海底目标物变得连续,背景噪声大大减少,自动提取结果准确可靠。 相似文献
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受海上环境、太阳高度角和无人船航向等因素的影响,无人船监控图像中海面反光现象不可避免,进而干扰后续视频处理,例如海天线的提取和运动目标的跟踪等。本文提出了一种无人船视频监控海面反光区域的检测及去除方法。首先,利用RGB颜色通道中的最大值与最小值获取像素点的亮度比例关系,检测海面反光区域中的高亮像素点。然后,针对检测到的高亮像素点进行形态学膨胀操作,获取较完整的海面反光区域。最后,结合反光区域中像素点亮度值随其位置分布的变化特点,使用加权后的邻域像素进行高亮成分抑制,实现反光区域的去除,并完成了无人船监视图像处理实验验证。实验结果显示,所提出的方法可有效检测并去除海面反光区域。 相似文献
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针对水雷目标探测识别困难这一现实问题,分析了水雷及其声呐图像的主要特点,介绍了基于统计特征、形态学、最大熵、分形和聚类等传统水雷识别方法和基于深度学习技术的水雷目标检测算法,以期为后续算法研发提供技术参考。 相似文献
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根据波浪图像的纹理特征,提出了一种基于数学形态学图像分割的波向检测方法,该方法首先利用顶帽变换和底帽变换,得到增强的波浪纹理图像,然后对图像波谷进行应用标记的分水岭分割,最后利用波谷形态方向提取出波浪方向,实验结果证明该算法能够有效地实现波向检测。 相似文献
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针对UUV避碰声呐探测障碍物过程中自主选择分割阈值进行障碍物检测的问题,提出了基于分区自适应阈值的障碍物检测算法。首先将避碰声呐图像均匀分为相同大小的图像块,对每个图像块基于最大类间方差算法确定该区域障碍物图像分割的高低阈值,然后对检测到的障碍物进行形态学处理去除孤立噪声点,对目标区域进行连通性分析及内部空洞处理,最终得到完整的障碍物轮廓信息。通过湖试数据验证表明了该方法对声呐障碍物检测的有效性。 相似文献
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针对传统舰载雷达和岸基雷达很难检测出海杂波背景下小目标的现状,提出一种基于局部回波幅值统计的方法来检测海杂波背景下小目标.该算法基于数学统计思想:计算不同距离单元海杂波的幅值的最大值和标准偏差,分析发现距离单元含有小目标回波的最大幅值明显小于其它距离单元只含有海杂波回波的最大幅值,距离单元含有小目标回波的幅值的标准偏差明显大于距离单元只含有海杂波回波的幅值的标准偏差,因此可以在对小目标没有任何先验知识条件下,较准确地检测出海杂波背景下的小目标.实验证明,该算法是有效的. 相似文献
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通过研究、分析现有基于遥感影像的舰船检测算法,提出了基于Itti视觉显著度模型改进的高分辨率遥感影像海面舰船检测方法,该方法利用海面舰船相对于海面背景信息视觉关注高的特点,参考视觉显著度模型计算原理,对高分辨率遥感影像中的颜色、亮度、方向、纹理等特征进行有效提取,然后利用归一化方法将不同特征融合,形成有利于舰船目标提取得视觉显著度图,最后对生成的视觉显著度图进行阈值分割和精细化处理获得最终的舰船检测结果。与传统舰船检测算法相比,本文算法能够有效检测复杂海面背景下的舰船目标,减少了单一特征变化对检测精度的影响,提高了检测准确率,具有较好的抗噪性和较强的鲁棒性。 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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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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舰船目标检测是进行海洋环境监管,保障海上权益的重要手段。基于深度学习的目标检测算法能在复杂环境下保持良好性能,为测试不同深度学习目标检测算法在舰船检测中的效果,本文构建了一个包含3893张图像的数据集,涵盖了复杂背景下不同类型的舰船,基于此数据集分别采用Faster RCNN、SSD、RetinaNet、YOLOv3、YOLOv4算法进行实验,结果表明,YOLOv4 、YOLOv3、RetinaNet、Faster RCNN平均精度均在83%以上,其中YOLOv4最高达到91.77%,Faster RCNN误检较多,而SSD平均精度最低,只有79.23%,总的舰船检测数目偏少。将5种模型训练结果在高分二号影像上进行测试,得到较好的检测效果,对舰船检测未来理论研究的开展具有一定的指导意义。 相似文献
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A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection 总被引:1,自引:0,他引:1
To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset. 相似文献