Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Jin, Hailin; Miska, Marc; Chung, Edward; Li, Maoxun; Feng, Yanming

doi:10.5772/45893

Cited by 9 publications

(6 citation statements)

References 55 publications

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“…In the algorithm proposed by Jin et al [20] the roads are extracted firstly and afterwards Gabor filters are applied in order to highlight for the lane markings. This step is followed by the thresholding algorithm of Otsu to achieve binary segmentation.…”

Section: Hd Maps and Aerial/satellite Imagery Literature Reviewmentioning

confidence: 99%

Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

Fischer

Azimi

Roschlaub³

et al. 2018

IJGI

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The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps. The authors give an overview about the current state in extracting road features from aerial imagery for creating HD maps, before shifting the focus of the paper towards remote sensing technology. Possible data sources and their relevant parameters are listed. A random forest classifier is used, showing how these data can deliver HD Maps on a country-scale, meeting specific quality parameters.

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Section: Hd Maps and Aerial/satellite Imagery Literature Reviewmentioning

confidence: 99%

Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

Fischer

Azimi

Roschlaub³

et al. 2018

IJGI

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“…Their results, however, have limited spatial accuracy and the resulting network has a lot of gaps and overlaps. Higher spatial accuracy was achieved by Jin et al (2012) with their method of detecting RSMs in aerial imagery using Gabor filters. They did not carry out further geometry analysis of RSMs; nor did they address fading RSMs or long gaps in RSM lines.…”

Section: Introductionmentioning

confidence: 99%

“…The method will be capable of modelling driving lanes in places where no RSMs are visible or detected. It uses road axes as a geometry source for the driving lanes leading to more accurate gap-bridging than in Jin et al (2012). Unlike Seo (2012), the geometry analysis is carried out in a vector environment, which leads to better computational performance and the possibility of using topology rules.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Driving–Lane Geometry Based on Aerial Images and Existing Spatial Data

Růžička¹,

Brůha²

2021

giforum

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Spatial data are a key element of geographic information systems (GIS). With the growing computational power of modern GIS, the demand for accurate and up-to-date high definition (HD) spatial data grows accordingly and increases the requirements of data acquisition. To simplify and automate the process of obtaining HD road data, several methods have been created with different approaches and stages of automation. A new method combining high resolution aerial images and existing linear road data is presented in this article. The method models roads in a vector environment at the level of single driving lanes. Object-based image analysis (OBIA) is used to identify road surface markings (RSMs) in aerial images; the geometry of RSM polygons is analysed (skeletonization, neighbourhood and context analysis, pattern recognition) in order to obtain a coherent network of driving lanes. The technique is able to distinguish automatically between solid and broken lines. The method proposed was tested and proven to satisfactorily model driving lanes, including in complex situations like junctions, roundabouts or over-or underpasses.

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“…In urban areas, the similarity between roads and surrounding objects is significant, because roads, sidewalks, building roofs, and parking lots are made of similar materials, such as asphalt, cement, and concrete, which have a similar appearance in the images [17]. Moreover, the images often contain building edges, roof features, vehicles, and other structures that can look like road markings in Remote Sens.…”

Section: Extracting Road Markings From Aerial Datamentioning

confidence: 99%

“…As the primary provider of static environment information for the intelligent vehicles, maps constructed through MMSs have even been considered as "virtual sensors" [14] that would enable long-distance autonomous navigation [15]. Advanced driver assistance systems (ADAS), which protect drivers and passengers through active, integrated safety, require maps to include precise sub-road details, such as the position and topology of pavement markings and stop lines, which are utilized for lane change assistance and lane departure warnings [16,17].…”

Section: Introductionmentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

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Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Cited by 9 publications

References 55 publications

Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

Automatic Detection of Driving–Lane Geometry Based on Aerial Images and Existing Spatial Data

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

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