Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalized cut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.     

Automatic Road Extraction from High Resolution Satellite Image using Adaptive Global Thresholding and Morphological Operations

Road network extraction from high resolution satellite images is one of the most important aspects. In the present paper, research experimentation is carried out in order to extract the roads from the high resolution satellite image using image segmentation methods. The segmentation technique is implemented using adaptive global thresholding and morphological operations. Global thresholding segments the image to fix the boundaries. To compute the appropriate threshold values several problems are also analyzed, for instance, the illumination conditions, the different type of pavement material, the presence of objects such as vegetation, vehicles, buildings etc. Image segmentation is performed using morphological approach implemented through dilation of similar boundaries and erosion of dissimilar and irrelevant boundaries decided on the basis of pixel characteristics. The roads are clearly identifiable in the final processed image, which is obtained by superimposing the segmented image over the original enhanced image. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image. The results can be used in automated map preparation, detection of network in trajectory planning for unmanned aerial vehicles. It also has wide applications in navigation, computer vision as a predictor-corrector algorithm for estimating the road position to simulate dynamic process of road extraction. Although an expert can label road pixels from a given satellite image but this operation is prone to errors. Therefore, an automated system is required to detect the road network in a high resolution satellite image in a robust manner.     

Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision. Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction. Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of the proposed approach.     

在高分辨率遥感影像中提取无清晰连续边缘线的道路

现有的许多道路提取算法均利用道路的外边缘线信息来实现道路的提取,当边缘线清晰连续时,采用这些方法都可以取得很好的提取效果.不过,在高分辨率的城市遥感影像中,常常会存在一些低对比度区域,处于其中的道路边缘线非常之弱,以致难以直接检测出单个的边缘点.如果受到树木、房屋及车辆的干扰,这些原本就很弱的边缘还会发生断裂.通过现有方法提取具有如此边缘线的道路难度很大.本文给出一种旨在解决这一问题的新方法.首先借鉴相位编组原理形成边缘线支持区并对其进行连接;然后利用动态规划方法从支持区中检测出边缘线并对这些线进行平滑;最后连接由边缘线构成的道路段,得出道路提取结果.实验表明,本方法可以较好地提取出无清晰连续边缘线的道路,对于边缘对比度较大的道路则可取得更为令人满意的结果.     

面向关系数据库的道路识别算法研究

为适应面向关系数据库的GIS应用开发,不再依赖GIS二次开发组件对Maplnfo电子地图进行空间数据处理,提出了在关系数据库中重新组织并存储电子地图的图元数据．进而展开相关空间数据处理算法的研究。本文针对电子地图中表征道路的线对象．以道路结点为基本存储单元,融合路段走向,创建了路网数据库,将对目标点周边道路的识别问题转化为对数据库中道路结点的条件查询问题。算法通过多次T—SQL查询,搜索目标点周边的候选路段,再利用其路段走向特征筛选出在目标点四个方位上的最佳匹配路段．进而生成路网状况的综合描述。实验结果表明,本算法对目标点周边路网的分布状况有较好的识别能力．识别率高、处理时间短且系统资源占用少。     

Maintaining accurate,current, rural road network data: An extraction and updating routine using RapidEye,participatory GIS and deep learning

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.     

Automatic urban road extraction using airborne laser scanning/altimetry and high resolution satellite data

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.     

利用词向量模型分析城市道路交通空间相关性

刻画城市道路之间的交通相关性是提高交通插值及预测水平的基础。现有研究及应用通常假设一定空间或拓扑距离内的道路相互之间具有相关性,这种方式忽视了道路之间交通影响的时空异质性。例如,上游道路交通流通常不会均匀扩散到所有下游道路,而是集中在特定方向上。道路之间产生交通影响和交互作用的根本原因是大量机动车辆穿梭其中。为从数据驱动的角度度量道路之间的交通相关性,从而顾及其时空异质性,本文利用词向量模型Word2Vec从大量机动车出行路径中挖掘道路之间的交通交互影响关系。首先把"路段-路径"类比为"词-文档";其次利用Word2Vec模型从大量路径(文档)中为每条路段(词)训练出一个实数向量(词向量);然后以向量之间的余弦相似度度量对应路段之间的交通相关性;最后利用交通状态数据对结果进行验证。以北京市200万条出租车出行路径为数据进行试验,结果表明:(1)平均水平上,向量相似度越高的邻近路段,其交通状态变化趋势也越相似,证明了本文方法可以正确度量道路之间的交通相关性,并刻画出其空间异质性;(2)工作日早、晚高峰及节假日路段之间的交通相关性大于工作日平峰和周六日,其合理性体现了本文方法可以正确捕捉道路交通相关性的时间异质性。本文方法及分析可为交通规划、诱导等提供方法论和理论基础。     

改进全卷积网络方法的高分二号影像农村道路提取

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。     

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.     

Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis

An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

基于网眼密度的道路选取方法

道路数据中的网眼密度能反映局部区域的道路密集程度,通过确定目标尺度要求的密度阈值,比例尺缩小后能够标识出数据中需要取舍路段的网眼;循环剥离密度最大的网眼,利用反映路段重要性的参数及其优先级,渐进筛选出舍弃的路段,并完成与邻接网眼的合并;得到的选取结果保持了道路网在密度、拓扑、几何及语义方面的重要特征,从而提出一种新的道路选取方法。最后进行实验,验证该方法的有效性。     

基于高分辨率遥感影像的汶川地震道路损毁评估

强烈地震造成道路基础设施的损毁,严重影响应急救灾工作.因此,及时准确地对灾区道路损毁情况进行评估,具有十分重要的现实意义.以汶川特大地震道路损毁评估工作为例,介绍了利用高分辨率卫星遥感影像进行地震灾害道路损毁评估的技术方法.通过对灾区20个县(市、区)国/省道基础设施损毁情况评估,结果表明,汶川、北川等6个县道路重度损毁,3个县(市)中度损毁,11个县(区)轻度损毁.表明该方法能够在应急期间缺乏地面调查的情况下,充分利用高分辨率遥感图像,对道路震害损毁进行快速及较为准确的评估.     

Fuzzy Logic System for Road Identification Using Ikonos Images

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     

改进剖面匹配和EKF对SAR影像道路半自动提取

道路提取作为典型的线状目标提取,是遥感影像目标解译的研究热点。合成孔径雷达（SAR）影像包含了丰富的物理特性,能够全天时、全天候地获取影像数据,已广泛应用于道路提取中。传统的道路提取方法分为全自动和半自动方法。全自动道路提取会出现漏检和错检,需要大量的人工后处理。半自动方法结合人工干预,是对计算机的计算能力和人工解译准确性的有效折中。提出了用一种改进剖面匹配和扩展卡尔曼滤波（EKF）的方法对SAR影像道路进行半自动提取的方法。首先构建了道路提取模型,其次通过改进剖面匹配算法获取准确的观测值,最后利用EKF对观测值进行更新获取道路最优估计值。选取美国缅因州Howland地区L波段UAVSAR数据和海南陵水地区X波段机载SAR数据进行实验,结果表明,该方法在较少人工干预的情况下,能够对复杂场景道路进行有效稳健的提取。     

多山地区输电通道内道路提取方法

准确提取输电通道内各地物类别是进行输电通道三维建模及交跨分析的基础。为解决目前贵州等多山地区输电通道内道路提取方法稳健性低、效率低的问题,本文通过结合道路特征显著性检测与形态约束的方法对影像中多山地区输电通道内道路的快速准确提取进行了研究。首先,对卫星影像中山区道路特征进行分析,对每个像素分别计算基于道路颜色一致性和结构一致性的像素级显著值;然后,结合显著目标空间先验知识融合显著性检测结果,形成最终道路显著图,初步提取影像中道路目标;最后,分析道路与建筑物等的差异性,基于道路形态一致性定义道路形态约束条件,通过制定约束规则最终实现道路的准确提取。结果表明,该方法对于不同弯曲程度、粘连程度及影像对比度的道路都能实现快速准确提取,提取完整度、正确率、质量及耗时平均分别为97.5%、97.0%、95.6%、0.515 s。该方法稳健性高,可以快速、准确提取各种情况下道路,在输电线路工程实际中有很好的应用前景。     

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

一种基于小波变换的遥感影像道路自动提取方法

遥感影像上道路的自动提取是摄影测量与遥感、计算机视觉等领域的重要课题。本文通过对图像小波系数的分析,完成道路节点的提取。随后利用基于小波变换的边缘提取算子,对道路进行边缘提取。边缘提取出来之后,以道路节点为种子点,对道路进行Snake跟踪。为保证提取效果,对图像进行了预处理。实验表明,利用本文提出的理论与方法能为道路网的自动提取与识别提供一个可靠的依据,同时也对其他地物（如建筑物）的自动提取提供一个有价值的参考。     

基于角度纹理特征及剖面匹配的高分辨率遥感影像带状道路半自动提取

提出了一种基于角度纹理特征及剖面匹配相结合的高分辨率遥感影像带状道路半自动提取方法.该方法由用户输入道路起点、初始方向及宽度,使用角度纹理特征模型预测初始的道路中线点,以抛物线方程参数构建道路中线轨迹参数模型.使用计算曲率变化的方法验证道路轨迹点,对验证失败的中线点位使用剖面匹配算法进行重新预测并确定,最终提取出该道路中线轨迹.本文使用Visual C 构建了原型系统,对QuickBird及IKONOS影像中具有一定宽度的带状道路进行了提取试验,并与经典的基于剖面匹配的半自动道路提取算法和基于Snakes的半自动道路提取算法进行了对比试验.经试验验证,本算法取得了较为理想的结果.