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基于区域生长的多尺度遥感图像分割算法 总被引:7,自引:0,他引:7
图像分割是图像解译的关键一步,仅仅利用光谱信息的传统分割方法已不能有效地对高分辨遥感图像进行分割。鉴于高分辨率遥感图像提供了地物光谱、形状和纹理等大量信息,文章提出了一种基于区域生长结合多种特征的多尺度分割算法。首先利用图像梯度信息选取种子点;其次综合高分辨率遥感图像地物的局部光谱信息和全局形状信息作为区域生长的准则进行区域生长。迭代这两个过程,直到所有区域的平均面积大于设定的尺度面积参数则停止生长。该算法用VC实现,实验结果表明该算法能获得不同尺度下的分割结果且分割效率高、分割效果好。 相似文献
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高分辨率遥感影像为用户提供了丰富的地表细节信息, 如何利用图像分析技术从高分辨率遥感影像中进行目标提取、更新地理信息数据库, 成为遥感信息处理研究的热点。传统的道路提取方法一般采用像素级检测方法, 仅利用了像素的光谱信息作为道路提取的依据, 无法利用影像的空间信息。提出了一种面向对象的高分辨率卫星影像道路提取方法, 并选取南京市IKONOS 影像进行了实验。首先, 对影像进行分割获取影像对象, 再通过对IKONOS 影像中道路特征的分析, 利用影像对象的光谱特征、几何特征和空间关系建立知识库, 最后, 利用知识库中的规则来提取影像中的道路。实验结果表明采用本方法能够较好地提取出实验区中的道路。 相似文献
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道路是现代交通的主要组成部分,对于管理和更新地理信息系统数据库中的道路信息非常重要。目前,自动提取道路网络的主要数据源为遥感图像数据,但随着近年来遥感影像的地面分辨率不断提高,图像中地物信息愈加丰富,对图像中道路信息的提取难度也随之增大。文章主要展开一种利用机器学习对高分辨率遥感图像的道路提取研究。首先对高分辨遥感图进行预处理,然后对图像进行特征提取,利用BP神经网络对特征进行训练,最后将需要道路提取的高分辨率遥感图区域分割。对每一个区域进行目标检测时,去除图像中非道路区域,并利用形态学方法提取出区域中的道路信息。实验表明,该方法在应对建筑物、植被等对道路提取有干扰时,识别效率明显提升。 相似文献
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为了解决在高分辨率遥感图像中易受同物异谱现象影响而导致河流信息提取不完整问题,依据河流区域在遥感图像中具有特定频率纹理信息这一特点,提出一种基于同态系统滤波的高分辨率遥感图像河流信息提取方法。该算法通过对遥感图像的频谱分析,确定河流信息在频域中的特征标识,采用一维剖面线加窗短时分析河流定位技术实现了全自动、快速全幅遥感图像河流定位和宽度估计,在同态系统下设计了低通滤波器实现对低频河流信息的提取。该算法采用"高分一号"遥感影像作为实验数据来源,相对于传统利用光谱信息的提取方法具有较高的提取精度,Kappa系数达0.85以上,并且实现全自动提取。实验结果表明所提算法能够快速、有效地提取复杂地貌区域内的河流信息。 相似文献
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基于待分割目标的灰度特征分布,提出了一种能自适应地改变生长准则参数的区域生长方法。将该自适应区域生长算法与GVF-Snake模型相结合用于高分辨率遥感影像道路提取,即用自适应区域生长方法提取出大致的道路区域,对生长出的道路图,利用数学形态学进行内部腐蚀并获得道路区域轮廓线,以该轮廓线作为GVF-Snake模型的初始轮廓,利用GVF-Snake模型进行道路跟踪,得到最终的道路提取结果。实验结果表明该方法能有效地提取高分辨率遥感影像中的道路目标,具有一定的实用性和鲁棒性。 相似文献
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为了克服单纯采用光谱信息提取河流的缺陷,利用高分辨率遥感影像突出的高分辨率的特性提出一种综合影像中光谱、纹理、几何特性等多特征联合提取河流的方法。该方法分别对河流水体的光谱特征、纹理特征及河流几何形状进行描述,选取特征参数,构造综合特征矩阵,利用均值聚类分割最终得到河流目标。通过对真实高分辨率遥感影像Worldview1影像进行的实验验证了该方法的高精准性及快速性。 相似文献
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《遥感信息》2016,(2)
针对实际应用中高分辨率遥感影像道路提取自动化程度低的现状,提出了一种半自动的高分辨率遥感影像道路提取方法。方法采用数据预处理、尺度分割、分类以及形态优化的工作流程,对高分一号遥感影像进行道路半自动提取。数据预处理利用NDWI、DNVI获得道路潜在区域,边缘增强突出道路边缘信息;采用多尺度分割切割道路潜在区域,尺度对比法获得道路最优分割尺度;主要依据道路的光谱特征、形状特征制定分类规则集进行分类;运用形态学开启运算、闭合运算优化道路形态。实验结果表明:在样本区域内提取精度达到90%,整景影像提取精度达到80%,且可推广到具有陕北地区地貌特征的高分一号影像道路快速提取应用中。 相似文献
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遥感图像自动道路提取方法综述 总被引:15,自引:1,他引:15
自动道路提取是遥感图像识别的重要研究领域. 实现自动化、智能化、可靠准确的图像道路提取对地理信息技术发展具有重要的应用价值和意义. 道路的物理属性和功能形成了道路的辐射特征、几何特征、拓扑特征和背景特征. 以该四类特征为线索, 介绍了自动道路提取的典型方法, 侧重于分析四类特征在道路提取中作用和应用方式. 简要介绍了自动道路提取的评估方法和准则, 列举了主流的道路提取软件和遥感图像片源, 展望了该领域的发展方向. 相似文献
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Simulating the psychological experience of human vision,a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly,based on the spectral and texture information,the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally,three types of identified road information are verified and merged,and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye,and the overall classification accuracy is higher,indicating that the algorithm is effective and feasible and has good use value. 相似文献
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This paper investigates road centreline extraction from high‐resolution imagery. A novel road detection system is proposed based on multiscale structural features and support vector machines (SVMs). The salient aspects of the strategy are: (1) structural features are exploited because road objects are narrow and extensive, with large perimeters and small radii; (2) the object‐based approach is used to extract multiscale information so as to reduce the local spectral variation caused by vehicles, shadows, road markings, etc.; (3) the hybrid spectral–structural features are analysed using the SVM classifier; and (4) multiple object levels are integrated because a multiscale approach can exploit the rich spatial information and detect multiscale road objects. Experiments were conducted on two IKONOS multispectral datasets and the results validated the proposed method. 相似文献
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针对复杂地形条件下道路特征选取不具代表性,分割精度低的问题,提出了一种基于卷积神经网络(PPMU-net)的高分辨率遥感道路提取的方法。将3通道的高分二号光谱信息与相应的地形信息(坡度、坡向、数字高程信息)进行多特征融合,合成6通道的遥感图像;对多特征的遥感图像进行切割并利用卷积网络(CNN)筛选出含道路的图像;将只含道路的遥感图像送进PPMU-net中训练,构建出高分辨率遥感图像道路提取模型。在与U-net神经网络、PSPnet神经网络相比时,所提的方法在对高分辨率遥感道路提取时能够达到较好的效果,提高了复杂地形条件下道路分割的精度。 相似文献
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Manohar Yadav Ajai Kumar Singh Bharat Lohani 《International journal of remote sensing》2017,38(16):4655-4682
Three-dimensional (3D) data of roadways are frequently used for extraction of detailed roadway information which is essential for several planning and engineering applications. Recent past has seen rapid growth in utilization of mobile LiDAR system (MLS) to acquire volumetric 3D data of roadway for this purpose. MLS data are capable of capturing highly detailed road information, which is useful for road maintenance and road safety operations. The existing literature shows that road environment complexity, unevenness, and absence of raised curb limit the extraction of road information from MLS data. It must be noted that a large number of roads, especially in developing world, are characterized by these complexities and thus raise the need for a technique which can work in these road environments. Considering the above, this paper proposes a method to extract road information, where road boundary is not geometrically well-defined. The proposed method is constructed using unstructured MLS data as input and does not require any other additional data. The method is divided into three major steps, that is, MLS data structuring and ground filtering, road surface point extraction, and road boundary refinement. The first step filters ground points from input MLS data, while the second step identifies road surface points from among the ground points. The second step is designed using specific characteristics of a road, that is, topology, surface roughness, and variation of point density. Third step refines road boundary. Three test sites, quite complex with heterogeneous characteristics, were used for demonstration of the proposed method. Road surfaces of these three roadways were accurately extracted without being affected by on-road objects and absence of raised curb. Average accuracy measures like completeness, correctness, and quality were found to be 93.8%, 98.3%, and 92.3%, respectively, in three test sites. Further, road boundaries of extracted road surfaces of these three test sites were refined at average completeness, correctness, and quality of 95.6%, 97.9%, and 93.7%, respectively. The proposed method has shown satisfactory performance for complex roadways having road section with and without raised curb, and has potential to be employed for such road environments, which are not uncommon. Proposed method was implemented on GPU-based parallel computing framework, which significantly saved the run time in processing of MLS data of three test sites. 相似文献
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Mehdi Maboudi Jalal Amini Michael Hahn Mehdi Saati 《International journal of remote sensing》2017,38(1):179-198
Increasing demands for up-to-date road network and the availability of very-high-resolution (VHR) satellite images as well as the popularity of high-speed computers provide motivation and preliminary materials for researchers to propose more advanced approaches in order to increase the automation and robustness of road extraction strategies. In this article, road characteristics are modelled via object-based image analysis (OBIA). Object-based information is embedded as heuristic information in the ant colony optimization (ACO) algorithm for handling the road network extraction problem. A new neighbourhood definition in object space is introduced, which affects the transition rule in order to decrease the road gaps. Furthermore, an innovative desirability function for ACO is designed, which extracts the road objects, competently. The experimental results demonstrate the efficiency of the proposed algorithm for road extraction from VHR images. Moreover, the results of two state-of-the-art methods are compared with the proposed algorithm. 相似文献
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基于总变分和形态学的航空图像道路检测算法 总被引:3,自引:0,他引:3
高分辨率航空图像中道路通常表现为较狭窄的面,这给分类算法创造了机会.文中提出了一种新的基于分类的航空图像道路自动提取方法--基于总变分和形态学分析方法,它首先根据邻域总变分和直方图得到分割道路所需的合适阈值并从图像中分割出道路区域,然后根据基于区域总变分和几何测度的准则函数及其模式频谱得到形态学普通开运算的阈值,最后用此准则及阈值对图像进行形态学普通开运算以去除和路面具有相似光谱特性的物体的干扰.初步实验证明,该方法具有良好的稳定性和较强的环境适应能力. 相似文献
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《遥感信息》2009,28(1):29-33
针对城市水体与建筑物阴影、沥青路面和浓密植被等暗地物的光谱混淆性,构建了结合光谱特征和空间特征的城市水体提取知识决策树。其基本思路为:首先
利用短波红外波段提取暗地物,其次分别利用浓密植被在近红外波段和沥青路面在红波段中的反射率剔除这两类暗地物,再次利用空间密度特征剔除建筑物阴影,最
后根据面积对水体进行补充识别。与现有方法相比,本方法提出了城市水体提取中需关注的暗地物类型并开展针对性特征分析,并利用由噪声环境下密度聚类方法
(DBSCAN)描述的空间密度特征区分城市水体和建筑物阴影。对北京城区SPOT 5多光谱影像开展的实验得到的检测率为86.18%,虚警率为13.82%,表明本方法是基于
中分辨率多光谱影像提取城市水体的有效方法。 相似文献
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基于Google影像的城市道路网提取及其应用 总被引:1,自引:0,他引:1
从遥感影像中提取道路信息制作专题图方法具有时效性强、周期短、操作快捷等特点。首先介绍了道路提取、处理以及专题图制作的方法与流程。其次以Google高分辨率影像为数据源,利用影像中道路的光谱和几何特征信息,结合计算机分类与形态学处理方法对实验区城市道路网络进行提取、处理和专题图的制作。实验结果表明:该方法能够清晰地提取出道路框架,满足一定的制图需求,而且对车辆、人群、阴影产生的道路内部空洞和边缘侵蚀具有很好的处理效果,具有较强的经济性和实用性。 相似文献