Object-Space Road Extraction in Rural Areas Using Stereoscopic Aerial Images

Póz, Aluir Porfírio Dal; Gallis, Rodrigo A. B.; Silva, João F. C. da; Martins, Érico Fernando de Oliveira

doi:10.1109/lgrs.2011.2177438

Cited by 27 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first group includes traditional methods based on surveying techniques or GPS to capture spatial data [8] as well as photointerpretation and manual digitization of roads (which use aerial/satellite images or the LiDAR-derived layers) [13]. Although these techniques are the most accurate and robust, they are also time-consuming and costly [14]. This group also includes participatory GIS; in a recent great study, a mobile application, RoadLab Pro, was used to automatically map the driving location of six different drivers [12].…”

Section: Brief Review Of the State-of-artmentioning

confidence: 99%

Forest Road Detection Using LiDAR Data and Hybrid Classification

et al. 2021

View full text Add to dashboard Cite

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.

show abstract

Section: Brief Review Of the State-of-artmentioning

confidence: 99%

Forest Road Detection Using LiDAR Data and Hybrid Classification

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Lv et al [31] proposed a multifeature sparsity-based model that can utilize multifeature complementation to extract roads from high-resolution imagery. Dal Poz et al [32] proposed a semiautomatic method to extract urban/suburban roads from stereoscopic satellite images. This method uses seed points to construct the road model in the object space.…”

Section: Related Workmentioning

confidence: 99%

“…The other idea involves treating the extraction as a network optimization problem. The road network is obtained by the connection of road seed points, and the final result is acquired with the use of graph theory or dynamic programming techniques [27][28][29][30][31][32]. The local features of the road (such as extensibility, edge characteristics, and topological structure of the road network, etc.)…”

Section: Introductionmentioning

confidence: 99%

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

et al. 2018

View full text Add to dashboard Cite

In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements.

show abstract

“…The framework combined the strengths of tensor encoding, feature extraction using Gabor Jets, and global optimization using Graph-Cuts. Poz et al (2012) proposed a semiautomatic 3D road extraction method in rural areas based on the dynamic programming algorithm.…”

Section: Introductionmentioning

confidence: 99%

AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

Guan

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

show abstract

Object-Space Road Extraction in Rural Areas Using Stereoscopic Aerial Images

Cited by 27 publications

References 13 publications

Forest Road Detection Using LiDAR Data and Hybrid Classification

Forest Road Detection Using LiDAR Data and Hybrid Classification

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

Contact Info

Product

Resources

About