共查询到20条相似文献，搜索用时 62 毫秒
1.
Poonam S. Tiwari H. Pande Ashwini Kumar Pandey 《Journal of the Indian Society of Remote Sensing》2009,37(2):223-231
Automatic road extraction from remotely sensed images has been an active research in urban area during last few decades. But
such study becomes difficult in urban environment due to mix of natural and man-made features. This research explores methodology
for semiautomatic extraction of urban roads. An integrated approach of airborne laser scanning (ALS) altimetry and high-resolution
data has been used to extract road and differentiate them from flyovers. Object oriented fuzzy rule based approach classifies
roads from high resolution satellite images. Complete road network is extracted with the combination of ALS and high-resolution
data. The results show that an integration of LiDAR data and IKONOS data gives better accuracy for automatic road extraction.
The method was applied on urban area of Amsterdam, The Netherlands. 相似文献
2.
一种从SAR图像中提取城市道路网络的方法 总被引：6，自引：0，他引：6
提出一种从高分辨率SAR图像中提取城市道路网络的算法.在高分辨率SAR图像中,道路在空间结构上表现为一细长的且宽度基本恒定不变的均匀区域.利用模糊C均值聚类方法对高分辨率SAR图像进行聚类分析,将道路类像素从原始图像中分离出来.为突出道路形状特征,减少冗余信息,对聚类结果进行细化,同时利用跟踪算子消除短线段;以提取道路中心线二值图的像素值作为图像能量,应用Snakes模型检测道路网络.通过实际SAR图像验证,该算法可以准确提取复杂的城市道路网络. 相似文献
3.
A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science. The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout. Such structural properties are modeled as an aggregation of parcels, buildings, and road networks. We introduce several computational measures (Ratio Minimum Distance, Minimum Ratio Minimum Distance, and Metric Compactness) that respectively evaluate the capability for a given road to be connected with the whole road network. These measures reveal emerging sub-network structures and point out differences between less-connective and more-connective parts of the network. Based on these local and global properties derived from the topological and graph-based representation, and on building density metrics, this paper proposes an analysis of road and building layouts at different levels of granularity. The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment. Overall, and by integrating the parcels and buildings layouts, this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout. 相似文献
4.
Mourad Bouziani Kalifa Goïta Dong-Chen He 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(1):143-153
The updating of geodatabases (GDB) in urban environments is a difficult and expensive task. It may be facilitated by an automatic change detection method. Several methods have been developed for medium and low spatial resolution images. This study proposes a new method for change detection of buildings in urban environments from very high spatial resolution images (VHSR) and using existing digital cartographic data. The proposed methodology is composed of several stages. The existing knowledge on the buildings and the other urban objects are first modelled and saved in a knowledge base. Some change detection rules are defined at this stage. Then, the image is segmented. The parameters of segmentation are computed thanks to the integration between the image and the geodatabase. Thereafter, the segmented image is analyzed using the knowledge base to localize the segments where the change of building is likely to occur. The change detection rules are then applied on these segments to identify the segments that represent the changes of buildings. These changes represent the updates of buildings to be added to the geodatabase. The data used in this research concern the city of Sherbrooke (Quebec, Canada) and the city of Rabat (Morocco). For Sherbrooke, we used an Ikonos image acquired in October 2006 and a GDB at the scale of 1:20,000. For Rabat, a QuickBird image acquired in August 2006 has been used with a GDB at the scale of 1:10,000. The rate of good detection is 90%. The proposed method presents some limitations on the detection of the exact contours of the buildings. It could be improved by including a shape post-analysis of detected buildings. The proposed method could be integrated into a cartographic update process or as a method for the quality assessment of a geodatabase. It could be also be used to identify illegal building work or to monitor urban growth. 相似文献
5.
Fuzzy Logic System for Road Identification Using Ikonos Images 总被引：1，自引：0，他引：1
Jalal Amini Caro Lucas Mohammad Saradjian Ali Azizi & Saeed Sadeghian 《The Photogrammetric Record》2002,17(99):493-503
Research into both extraction of man-made objects and automatic change detection from aerial and satellite images has made significant progress in recent years. This paper presents an approach, based on a fuzzy logic system, for the identification of suburban roads in Ikonos images. The linguistic variables are the mean and standard deviation (SD) of different objects with Gaussian membership function. After the roads have been identified provisionally and their skeleton extracted, the skeleton can be vectorised and then used as direct input to a GIS for further analysis. The method was tested on an Ikonos "Geo" image covering Bilesavar in north-western Iran. For the suburban area of Bilesavar, the results showed that grey scale values ranged from 20 to 190 for non-roads and 226 to 228 for roads, the optimum width of the Gaussian kernel function was 3 and the SD was 0.4. It was also found that about 91% of main roads with a width of 6 to 12 pixels could be extracted from high resolution satellite imagery by the algorithm. The computer program for this study has been developed in visual C++ based on Windows 98 相似文献
6.
Improving urban road extraction in high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts 总被引：1，自引：0，他引：1
In this letter, the problem of detecting urban road networks from high-resolution optical/synthetic aperture radar (SAR) images is addressed. To this end, this letter exploits a priori knowledge about road direction distribution in urban areas. In particular, this letter presents an adaptive filtering procedure able to capture the predominant directions of these roads and enhance the extraction results. After road element extraction, to both discard redundant segments and avoid gaps, a special perceptual grouping algorithm is devised, exploiting colinearity as well as proximity concepts. Finally, the road network topology is considered, checking for road intersections and regularizing the overall patterns using these focal points. The proposed procedure was tested on a pair of very high resolution images, one from an optical sensor and one from a SAR sensor. The experiments show an increase in both the completeness and the quality indexes for the extracted road network. 相似文献
7.
Elahe Khesali Mohammad Javad Valadan Zoej Mehdi Mokhtarzade Maryam Dehghani 《Journal of the Indian Society of Remote Sensing》2016,44(1):21-29
Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm. 相似文献
8.
利用GLCM纹理分析的高分辨率SAR图像建筑区检测 总被引：4，自引：0，他引：4
根据高分辨率SAR图像上建筑区的影像特征, 提出了基于灰度共生矩阵(gray-level cooccurrence Matrix, GLCM)纹理分析的建筑区提取方法, 该方法由初步定位和边界调整2个步骤组成, 均遵循特征计算、基于Bhattacharyya距离的特征选择和KNN分类流程, 所不同的是2个步骤中分别采用了逐块和逐点计算纹理特征的方式以兼顾纹理分析的效率和准确性。文中对不同SAR传感器获取的图像进行了实验。实验结果表明, 选用具有最大Bhattacharyya距离值的3或4个特征可以获得较好的初步定位结果, 建筑区的检测率超过80%, 虚警率低于10%;随着边界调整的进行, 检测到的建筑区边界逐渐接近于真实边界。实验结果验证了该算法的有效性。 相似文献
9.
全极化SAR数据反演桥面高度 总被引：1，自引：0，他引：1
根据高分辨率SAR图像上建筑区的影像特征, 提出了基于灰度共生矩阵(gray-level cooccurrence Matrix, GLCM)纹理分析的建筑区提取方法, 该方法由初步定位和边界调整2个步骤组成, 均遵循特征计算、基于Bhattacharyya距离的特征选择和KNN分类流程, 所不同的是2个步骤中分别采用了逐块和逐点计算纹理特征的方式以兼顾纹理分析的效率和准确性。文中对不同SAR传感器获取的图像进行了实验。实验结果表明, 选用具有最大Bhattacharyya距离值的3或4个特征可以获得较好的初步定位结果, 建筑区的检测率超过80%, 虚警率低于10%;随着边界调整的进行, 检测到的建筑区边界逐渐接近于真实边界。实验结果验证了该算法的有效性。 相似文献
10.
城市道路的高精度提取可为城市三维表达、城市地形分析、城市建设规划、交通导航等提供数据基础和支撑。本文以合肥市局部城区为试验区,以开源路网、街景图像和遥感影像为数据源,在利用最大似然法进行初提取的基础上,通过空间分析、统计分析、几何量测、最小二乘拟合等方法进行粘连分割、缺失处理和交叉口细化等关键处理,构建了多源数据协同的城市道路提取方法,并对提取结果进行了精度评价和分析。试验结果表明,本文提出的城市道路提取方法优于最大似然和面向对象方法,提取总体精度为96.65%,Kappa系数为93.71%,道路宽度偏离标准差为0.03m,特别是对同物异谱、同谱异物及遮挡等造成的信息提取不全问题具有良好的改善效果。 相似文献
11.
自发地理信息(VGI)是一种新兴的地理数据采集方式,具有数据更新快、细节丰富、覆盖范围广等优势。利用VGI数据可以对道路网实现快速更新;但是由于VGI数据是非专业自发共享的,且数据采集时多使用非专业设备,所以存在数据质量不高的问题。大量VGI数据对同一地理要素的重复采集与融合处理则可改善数据的质量,文中以多人采集的道路网数据为例,结合矢量要素的匹配与融合理论,设计一种适用于道路网VGI数据的匹配与融合算法。首先在路段结点处建立缓冲区进行结点匹配,再根据路段距离相似度进行路段匹配,最后再利用Delaunay三角剖分融合算法对匹配后的同名路段进行融合。将匹配融合后的道路网与原始道路网VGI数据及Google影像图叠加对比分析,结果表明利用本文算法可有效地实现道路网VGI数据的匹配与融合。 相似文献
12.
针对目前利用高分遥感数据提取农村道路的研究与应用少,提取结果精准度不够的问题,提出了结合空洞卷积和ASPP（Atrous Spatial Pyramid Pooling）结构的改进全卷积农村道路提取网络模型DC-Net（Dilated Convolution Network）。该模型基于全卷积的编解码结构来提取道路深度特征信息,同时针对农村道路细长的特点,在解编码层之间加入了以空洞卷积为基础的ASPP（Atrous Spatial Pyramid Pooling）结构来提取道路的多尺度特征信息,在不牺牲特征空间分辨率的同时扩大了特征感受野FOV（Field-of-View）,从而提高细窄农村道路的识别率。以长株潭城市群郊区部分区域为试验对象,以高分二号国产卫星遥感影像为实验数据,将本文提出的方法与经典的几种全卷积网络方法进行实验结果对比分析。实验结果表明：（1）本文所提出的道路提取模型DC-Net在农村道路的提取上具有可行性,整体提取平均精度达到98.72%,具有较高的提取精度;（2）对比几种经典的全卷积网络模型在农村道路提取上的效果,DC-Net在农村道路提取的精度和连结性、以及树木和阴影的遮挡方面,均表现出了较好的提取结果;（3）本文提出的改进全卷积网络道路提取模型能够有效地提取高分辨率遥感影像中农村道路的特征信息,总体提取效果较好,为提高基于国产高分影像的农村道路提取精度提供了一种新的思路和方法。 相似文献
13.
Image segmentation is one of key steps in object based image analysis of very high resolution images. Selecting the appropriate scale parameter becomes a particularly important task in image segmentation. In this study, an unsupervised multi-band approach is proposed for scale parameter selection in the multi-scale image segmentation process, which uses spectral angle to measure the spectral homogeneity of segments. With the increasing scale parameter, spectral homogeneity of segments decreases until they match the objects in the real world. The index of spectral homogeneity is thus used to determine multiple appropriate scale parameters. The performance of the proposed method is compared to a single-band based method through qualitative visual interpretation and quantitative discrepancy measures. Both methods are applied for segmenting two images: a QuickBird scene of an urban area within Beijing, China and a Woldview-2 scene of a suburban area in Kashiwa, Japan. The proposed multi-band based segmentation scale parameter selection method outperforms the single-band based method with the better recognition for diverse land cover objects in different urban landscapes. 相似文献
14.
Guillaume Touya 《Transactions in GIS》2010,14(5):595-614
In the context of geographical database generalization, this article deals with a generic process for road network selection. It is based on the geographical context, which is made explicit, and on the preservation of characteristic structure. It relies on literature that is adapted and collected. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows the initial dataset to be enriched with explicit geographic structures that were implicit in the initial data. It helps both to make the geographical context explicit and to preserve characteristic structures. Then this enrichment is used as knowledge input for the following step: that is, the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity? and tested on large datasets. 相似文献
15.
针对严重污染的城市水体与道路、建筑物、阴影等易于混分,以及遥感水体提取结果不连续、存在斑点问题,本文以广州市流溪河与东江水系为研究对象,基于2016年与2017年OLI遥感影像,采用本文新提出的城市水体指数法（CWI）,同时结合分形几何算法,通过设置形状面积等特征,实现城市复杂环境下的水体信息的自动提取。并与单通道算法、改进的归一化差异水体指数（MNDWI）算法、支持向量机法（SVM）与光谱角度法的水体提取结果进行对比分析。结果表明：SVM算法出现大量斑点,其次为MNDWI水体指数算法,光谱角度算法与单通道算法斑点较少,但水体提取结果不连续,部分河道漏分。本文提出的算法能够克服山体阴影、道路、建筑物等影响,实现城市污染水体以及一般水体连续、准确提取。本文的提取结果可为水资源调查、洪水灾害预测评估、水利规划、环境监测等工作提供基础数据支撑。 相似文献
16.
《International Journal of Digital Earth》2013,6(2):133-153
Abstract When road symbols are shown in a size proportionate to the reduced scale, several problems of legibility may arise concerning the urban blocks. By the method proposed in this paper, in order to overcome these problems, urban block areas are enlarged through amalgamation and the intervening roads in the amalgams are eliminated. This method includes two new approaches for computation of threshold used in determination of important roads based on the connectivity measure, and for definition of minimum block space and area requirements based on graphic limits. A block life cycle was designed for amalgamation of blocks. For the amalgamation process, a new algorithm was developed. The experimental testing indicates that important roads and the roads surrounding the urban area have been preserved. A simpler and more legible road network has been acquired. This method can be described as a more holistic approach as the buildings are taken into account. The problems arising in the experimental testing indicate that the cartographic selection/elimination process is not sufficient by itself for road network generalization, which can be assumed as one of the integral parts of Digital Earth. As well as the selection/elimination, processes such as displacement and caricaturizing are also needed. 相似文献
17.
M. Izadi A. Mohammadzadeh A. Haghighattalab 《Journal of the Indian Society of Remote Sensing》2017,45(6):965-977
Tracking damaged roads and damage level assessment after earthquake is vital in finding optimal paths and conducting rescue missions. In this study, a new approach is proposed for the semi-automatic detection and assessment of damaged roads in urban areas using pre-event vector map and both pre and post-earthquake QuickBird images. In this research, damage is defined as debris of damaged buildings, presence of parked cars and collapsed limbs of trees on the road surface. Various texture and spectral features are considered and a genetic algorithm is used to find the optimal features. Subsequently, a support vector machine classification is applied to the optimal features to detect damages. The proposed method was tested on QuickBird pan-sharpened images from the Bam earthquake and the results indicate that an overall accuracy of 93% and a kappa coefficient of 0.91 were achieved for the damage detection step. Finally, an appropriate fuzzy inference system (FIS) and also an “Adaptive Neuro-Fuzzy Inference System” are proposed for the road damage level assessment. These results show that ANFIS has achieved overall accuracy of 94% in comparison with 88% of FIS. The obtained results indicate the efficiency and accuracy of the Neuro-Fuzzy systems for road damage assessment. 相似文献
18.
Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained. 相似文献
19.
20.
基于遥感数据SOFM网络分类的五种城市增长方式鉴别方法应用研究 总被引：5，自引：0，他引：5
使用了自主研发的自组织神经网络分类(SOFM)方法，选择了1988、1994、2001和2003年5～6月份TM^ 时间序列多光谱遥感数据，对北京城市增长方式进行了30m分辨率遥感时序数据的鉴别，包括填充式增长方式、扩张式增长方式、独立式增长方式、线状式增长方式和簇状增长方式，并绘制了三个时期的城市增长图。在此基础上，根据北京城市增长环线驱动的特点，分别对四环内、四环至五环、五环至六环1988～1994年、1994～2001年、2001～2003年的5种城市扩展方式面积进行了统计。 相似文献