基于种子点增长的SAR图像海岸线提取算法

合成孔径雷达SAR(Synthetic Apenure Radar)图像海岸线检测在自动导航、地图绘制等海洋应用方面具有重要意义。国内外针对海岸线检测提出了不少方法,但是这些方法都有一个很大的缺点:速度慢。为此提出了一种快速SAR图像海岸线提取算法。该算法基于种子点增长的思想,从一个选定的初始种子点出发,计算出所有连通的海洋区域点,从而可以确定出海岸线位置。将该算法应用于真实的SAR图像进行实验,证明了该算法的有效性。     

基于改进水平截集算法的SAR图像海岸线检测

合成孔径雷达(SAR)图像海岸线检测,在自动导航、地图绘制等海洋应用方面具有重要意义。水平截集算法是一种基于人类视觉特性的边缘检测方法。由于它具备检测效果好、抗噪能力强等优点,因而在海岸线检测方法的研究和应用两方面倍受关注。然而,水平截集算法由于迭代方式复杂等原因在应用于分辨率较大的图像时,检测速度比较慢,限制了它在工程应用中的实用性。针对SAR图像,提出一种基于水平截集算法的改进算法,先在低分辨率图像中用水平截集算法进行粗略检测,得到贴近真实海岸线的轮廓线,然后将轮廓线映射到高分辨率图像上,继续用水平截集算法进行检测,最后得到精确的结果。实验中使用Radarsat ScanSAR图像证明该方法可大大加快检测速度,通过与原水平截集算法的检测效果进行对比,新方法没有降低检测效率。     

基于平稳小波变换的SAR图像海岸线提取

阐述了一种利用平稳小波变换(SWT)从SAR图像中提取海岸线的方法。该方法首先利用基于局部统计特性的自适应滤波算法对SAR图像进行滤波,然后利用SWT对SAR图像进行分析处理,计算SWT系数的小波梯度信息,通过模极大值搜索检测边缘点,最后利用阈值化和形态学方法对局部极大值图像进行细化处理。实验结果证明,这种方法是对于SAR图像海岸线提取是有效的。     

SAR图像船只尾迹检测研究综述

利用星载合成孔径雷达(SAR)图像进行海面船只尾迹检测的研究在海洋领域具有重要意义。SAR海洋图像中尾迹线性特征的检测和精确定位是个难点,研究了SAR图像中船只尾迹的检测方法,根据近年来SAR图像海面船只尾迹检测的发展,总结了尾迹类型及其成像机理,分析了影响尾迹检测的因素和目前国内外的尾迹检测算法,通过对这些方法的对比分析得出它们的优缺点,并对今后的研究方向进行了展望。     

基于Mumford-Shah模型和开样条曲线的边界检测

受Cremers方法启发, 本文提出了一种新的开边界自动检测算法, 如图像中海岸线和天际线的检测. 这一算法的设计主要是基于样条函数、曲线演化理论和Mumford-Shah图像分割泛函模型. 由于所要检测的目标为图像区域中开曲线, 在一般Mumford-Shah模型中引入了两个约束条件. 这就将开边界的检测问题转化为一般的曲线最小分割问题. 通过样条曲线控制点所满足的微分方程和约束条件, 曲线将演化至所要求的边界. 如果图像中有一条开曲线将图像分为两个明显不同质区域, 这一算法将能有效地自动检测出该边界曲线, 且不需要边界的梯度信息. 即使在图像中有大量噪声情况下, 该算法同样有效. 此外, 通过两条曲线演化方程, 该算法可推广到图像中带状区域的(如河流、道路等)自动检测.     

SAR图像中海上舰船目标自动检测新方法

针对中分辨率近岸海域SAR图像,结合已有的舰船检测算法,提出了一种新的海上舰船目标自动检测方法。该方法先根据相应的抽取算法和图像数据映射准则,分离图像中的海洋和陆地区域,并结合最大熵分割法提取海洋背景中包含候选目标的感兴趣区域,最后利用特征匹配方法检测出真正的舰船目标。对50多幅SAR图像进行了试验,其结果表明该方法能自动、快速、准确地检测出图像中舰船目标。     

基于海图信息的SAR影像海陆自动分割

海陆分割是对海洋SAR图像进行船舶检测的基本前提之一,传统的手动分割方法可以很精确,但操作繁琐,速度较慢,不适合于批量图像的处理,而海岸线检测算法抗噪能力较低,对于近海多岛屿地区检测效果较差。提出了一种基于海图信息的SAR影像海陆自动分割方法,该方法利用SAR图像的地理信息,有效地与基于地理先验信息的海洋区域矢量图层相叠加,将对SAR图像的海陆分割问题转换为对矢量图层中多边形矢量元素区域的判断,并使海陆分割问题实现了自动化。最后使用Radarsat-1数据和ALOS PALSAR数据对该方法进行了验证。实验表明,近似自动分割与手动分割效果非常接近,对船舶检测几乎没有影响,且运行速度较快,适于图像的实时处理。
     

SAR图像海洋表面油膜检测方法

海洋表面油膜对海洋环境影响极大,因此,及时获取海面油膜信息对保护海洋具有重要意义。目前各国采用的油膜检测方法主要有直接探测法和遥感方法。其中,遥感方法中的合成孔径雷达(SAR)是目前研究的热点。总结了SAR图像应用于海面油膜检测的主要特点,介绍并分析比较了SAR图像油膜检测的一般步骤及其实现方法。最后提出了SAR图像海洋表面油膜检测的发展方向。     

SAR图像海洋表面油膜检测方法

海洋表面油膜对海洋环境影响极大,因此,及时获取海面油膜信息对保护海洋具有重要意义。目前各国采用的油膜检测方法主要有直接探测法和遥感方法。其中,遥感方法中的合成孔径雷达(SAR)是目前研究的热点。总结了SAR图像应用于海面油膜检测的主要特点,介绍并分析比较了SAR图像油膜检测的一般步骤及其实现方法。最后提出了SAR图像海洋表面油膜检测的发展方向。     

一种基于矩不变的SAR海洋图像舰船目标检测算法

提出了一种先分离SAR图像场景中的陆地和海洋区域，再利用矩不变自动门限法对分离出的海洋子场景进行舰船目标检测的算法。对数据处理的结果表明，该方法能够有效、快速、准确地检测到SAR海洋图像中的舰船目标。     

River detection algorithm in SAR images based on edge extraction and ridge tracing techniques

This article presents a novel river detection algorithm in synthetic aperture radar (SAR) images. It is based on edge extraction in the wavelet domain followed by ridge tracing to merge the water region. The edge detection is approached by direct spatial correlation of wavelet transform data at several adjacent scales. For the ridge tracing algorithm, the concept used in fingerprint identification is introduced to complete riverbank linking and connecting based on a greyscale image. This improvement avoids the disadvantages of the widely used snake model in coastline connection. As indicated by the river detection results from the real SAR images, our river detection algorithm is efficient and robust in detecting the river in complicated suburb and nature water areas.     

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.     

基于深度学习的海岸线边缘检测网络模型

海岸线的动态监测对海岸带的规划管理具有非常重要的意义。由于海陆环境错综复杂,遥感影像中海陆边界光谱特征不明显,导致提取的海岸线定位不准确。提出一种融合语义分割网络和边缘检测网络的深度卷积神经网络模型(EWNet)。该模型包含2个分支流：语义分割流负责提取分层语义信息并用来指导边缘检测流获取岸线语义信息;边缘检测流通过语义分割流完善边缘语义信息。在“高分一号”遥感图像上的实验结果表明,与几种最新网络模型相比,EWNet获得了更精确的海岸线边界提取结果。     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

基于卫星影像技术的海岸线水域变化高精度检测系统设计

针对目前研究的海岸线水域变化检测系统在检测过程中,存在系统检测精度较低,检测稳定性较差,检测时间较长的问题,设计了基于卫星影像技术的海岸线水域变化高精度检测系统。采用STM32C8T6为主控芯片的主控器,进行高效的数据处理和网关通讯,以多光谱传感器为核心检测设备,通过采集光学数据,生成海岸线水域图像。利用XL1509-5.0芯片为核心的外接电源,供给系统电源设备,以OUTPUT作为电源开关的输出引脚,便于主控器对电源进行控制,完成系统硬件设计。采用卫星影像技术,对采集图形进行滤波处理、降噪处理以及图像二值化计算,嵌入Linux操作系统和MySQL数据库,高精度检测采集图像,完成系统软件设计,实现海岸线水域变化高精度检测。实验结果表明,设计的基于卫星影像技术的海岸线水域变化高精度检测系统的检测稳定性较好,能够有效实现海岸线水域变化高精度检测,缩短海岸线水域变化检测时间。     

An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery

The use of synthetic aperture radar (SAR) imagery is generally considered to be an effective method for detecting surface water. Among various supervised/unsupervised classification methods, a SAR-intensity-based histogram thresholding method is widely used to distinguish waterbodies from land. A SAR texture-based automatic thresholding method is presented in this article. The use of texture images substantially enhances the contrast between water and land in intensity images. It also makes the method less sensitive to incidence angles than intensity-based methods. A modified Otsu thresholding algorithm is applied to selected sub-images to determine the optimal threshold value. The sub-images were selected using k-means results to ensure a sufficient number of pixels for both water and land classes. This is critical for the Otsu algorithm being able to detect an optimal threshold for a SAR image. The method is completely unsupervised and is suitable for large SAR image scenes. Tests of this method on a Radasat-2 image mosaicked from 8 QuadPol scenes covering the Spritiwood valley in Manitoba, Canada, show a substantial increase in land–water classification accuracy over the commonly used SAR intensity thresholding method (kappa indices are 0.89 vs. 0.79). The method is less computationally intensive and requires less user interaction. It is therefore well suited for detecting waterbodies and monitoring their dynamic changes from a large SAR image scene in a near-real time environment).     

Coastline interpretation from multispectral remote sensing images using an association rule algorithm

On the basis of the Apriori algorithm, a class association rule algorithm is presented. A sea–land separation method was designed, and then a shoreline detection method proposed for interpreting multispectral remote sensing images. When separating the land from the sea, not only the spectral attributes but also the texture attributes and basic statistical values were considered in attribute space. To test the feasibility of the method, a Landsat Enhanced Thematic Mapper Plus (ETM+) image scene was used to interpret the coastline. First, the association rules of the sea–land separation of the study area were discovered from learning samples by using the class association rule algorithm. Second, the sea and the land of the image were separated with the mined rules. Third, the coastline was interpreted from the separation result. The accuracy of the interpretation result was computed with a proposed line matching accuracy evaluation algorithm. We show that the proposed method can interpret the coastline accurately and does not require any complex preprocessing.