Object-based road extraction from satellite images using ant colony optimization

Maboudi, Mehdi; Amini, Jalal; Hahn, Michael; Saati, Mehdi

doi:10.1080/01431161.2016.1264026

Cited by 42 publications

(28 citation statements)

References 34 publications

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“…(4) If the reference point is a manually input point, the gradient is used as a constraint, and the process proceeds to step (5). If the reference point is the matching point, the known radius of the road is selected as the constraint condition, and the process proceeds to step (6).…”

Section: Adaptive Correction Modelmentioning

confidence: 99%

“…Segmentation is the core of this type of method. Commonly used segmentation models include threshold segmentation [3], multi-scale segmentation [4,5], fuzzy C-means [6], graph segmentation [7], edge segmentation [8], and the ISODATA algorithm [9]. For example, Maboudi et al [4] applied multi-scale models combining color and shape information to segment images; classified the segmentation units based on structural, spectral, and textural characteristics; and applied the tensor voting method to connect road fractures.…”

Section: Introductionmentioning

confidence: 99%

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Lane-Level Road Extraction from High-Resolution Optical Satellite Images

et al. 2019

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High-quality updates of road information play an important role in smart city planning, sustainable urban expansion, vehicle management, urban planning, traffic navigation, public health and other fields. However, due to interference from road geometry and texture noise, it is difficult to avoid the decline of automation while accurately extracting roads. Therefore, we propose a high-resolution optical satellite image lane-level road extraction method. First, from the perspective of template matching and considering road characteristics and relevant semantic relations, an adaptive correction model, an MLSOH (multi-scale line segment orientation histogram) descriptor, a sector descriptor, and a multiangle beamlet descriptor are proposed to solve the interference from geometry and texture noise in road template matching and tracking. Second, based on refined lane-level tracking, single-lane and double-lane road-tracking modes are designed to extract single-lane and double-lane roads, respectively. In this paper, Pleiades satellite and GF-2 images are selected to set up different scenarios for urban and rural areas. Experiments are carried out on the phenomena that restrict road extraction, such as tree occlusion, building shadow occlusion, road bending, and road boundary blurring. Compared with other methods, the proposed method not only ensures the accuracy of lane-level road extraction but also greatly improves the automation of road extraction.

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Section: Adaptive Correction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lane-Level Road Extraction from High-Resolution Optical Satellite Images

et al. 2019

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show abstract

“…High quality and updated road network maps provide important information for many domains, e.g., driving assistance systems, transportation management, smart city planning, and Geographic Information System (GIS). There is a large body of literature about road extraction from very high resolution (VHR) images (Mena, 2003;Quackenbush, et al, 2013;Wang et al, 2016;Maboudi et al, 2017). However, to the best of our knowledge, there are few comprehensive studies focusing on filling gaps of the extracted road network and analyzing its effect on the overall quality of the network.…”

Section: Introductionmentioning

confidence: 99%

Impact of Gap Filling on Quality of Road Networks

Maboudi

Amini

Gerke

2019

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

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High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications

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“…The most popular region-based methods first segment images into regional objects via typical segmentation algorithms such as graph cut [20], energy functional analysis [21], the watershed algorithm [22], or a support vector machine (SVM)-based method [23,24]. For segmented objects, Shi et al [7,25] and Lei et al [26] used shape features to judge the segmented regions of road or nonroad objects.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

Chen

Zhu

Xie

et al. 2018

IJGI

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Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

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Object-based road extraction from satellite images using ant colony optimization

Cited by 42 publications

References 34 publications

Lane-Level Road Extraction from High-Resolution Optical Satellite Images

Lane-Level Road Extraction from High-Resolution Optical Satellite Images

Impact of Gap Filling on Quality of Road Networks

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

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