Automatic road-side extraction from large scale imagemaps

Amini, Jalal; Saradjian, Mohammad Reza; Blais, J. A. R.; Lucas, C.; Azizi, Ali

doi:10.1016/s0303-2434(02)00004-1

Cited by 51 publications

(23 citation statements)

References 12 publications

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“…A solution to join these line segments is suggested by Dell'Acqua et al, 2002, Gamba et al, 2006, Mohan and Nevatia, 1992, Hu and Tao, 2007, Amini et al, 2002 technique called Perceptual Grouping and perhaps an alternative in future research. The experiment conducted with the synthetic image shows the efficiency of the Snakes method when the seed points are marked near of the road, mostly, converging exactly to the central axis of the roads.…”

Section: Resultsmentioning

confidence: 99%

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

Lotte

Sant’Anna

Almeida

2013

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

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ABSTRACT:Research works dealing with computational methods for roads extraction have considerably increased in the latest two decades. This procedure is usually performed on optical or microwave sensors (radar) imagery. Radar images offer advantages when compared to optical ones, for they allow the acquisition of scenes regardless of atmospheric and illumination conditions, besides the possibility of surveying regions where the terrain is hidden by the vegetation canopy, among others. The cartographic mapping based on these images is often manually accomplished, requiring considerable time and effort from the human interpreter. Maps for detecting new roads or updating the existing roads network are among the most important cartographic products to date. There are currently many studies involving the extraction of roads by means of automatic or semi-automatic approaches. Each of them presents different solutions for different problems, making this task a scientific issue still open. One of the preliminary steps for roads extraction can be the seeding of points belonging to roads, what can be done using different methods with diverse levels of automation. The identified seed points are interpolated to form the initial road network, and are hence used as an input for an extraction method properly speaking. The present work introduces an innovative hybrid method for the extraction of roads centre-axis in a synthetic aperture radar (SAR) airborne image. Initially, candidate points are fully automatically seeded using Self-Organizing Maps (SOM), followed by a pruning process based on specific metrics. The centre-axis are then detected by an open-curve active contour model (snakes). The obtained results were evaluated as to their quality with respect to completeness, correctness and redundancy.

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Section: Resultsmentioning

confidence: 99%

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

Lotte

Sant’Anna

Almeida

2013

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

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“…Admittedly, it is not always employed as a form of postprocessing to improve the final output, but equally during the workflow to smooth the result of a workflow step before further processing [73,120,121,126]. When applied at the end of the workflow in case studies on road extraction, morphological filtering is often combined with skeletons to extract the vectorized centerline of the road [79,125,142,196,198].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Morphological operators are employed as a postprocessing step to smooth the contour of detected line features [195]. Morphological operators [73,75,79,115,116,120,121,126,128,146,148,196,198,199] [132] [124,125,142,144,200] Remote Sens. 2016, 8, 689…”

Section: Postprocessingmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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“…The proposed method is fairly simple and fully automatic, but the experiments are limited to the extraction of highways in rural areas. Amini et al (2002) utilize a segmentation method called the split and merge algorithm to automatically extract roadsides from large-scale image maps. The proposed method consists of two stages: i) straight lines extraction, and ii) roads skeleton extraction.…”

Section: Rural Road Extraction Techniquesmentioning

confidence: 99%

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

Jin¹,

Miska²,

Chung³

et al. 2012

Remote Sensing - Advanced Techniques and Platforms

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Automatic road-side extraction from large scale imagemaps

Cited by 51 publications

References 12 publications

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

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

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