• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.301-303
• /
• 2003

The extraction of 3D road network from high-resolution aerial images is still one of the current challenges in digital photogrammetry and computer vision. For many years, there are many researcher groups working for this task, but unt il now, there are no papers for doing this with TLS (Three linear scanner), which has been developed for the past several years, and has very high-resolution (about 3 cm in ground resolution). In this paper, we present a methodology of road extraction from high-resolution digital imagery taken over urban areas using this modern photogrammetry’s scanner (TLS). The key features of the approach are: (1) Because of high resolution of TLS image, our extraction method is especially designed for constructing 3D road map for next -generation digital navigation map; (2) for extracting road, we use the global context of the intensity variations associated with different features of road (i.e. zebra line and center line), prior to any local edge. So extraction can become comparatively easy, because we can use different special edge detector according different features. The results achieved with our approach show that it is possible and economic to extract 3D road data from Three Linear Scanner to construct next -generation digital navigation road map.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.79-82
• /
• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.4
• /
• pp.346-352
• /
• 2011

This paper presents a system of road extraction for traffic images from a single camera. The road in the images is subject to large changes in appearance because of environmental effects. The proposed system is based on the integration of color correlograms and random forest. The color correlogram depicts the color properties of an image properly. Using the random forest, road extraction is formulated as a learning paradigm. The combined effects of color correlograms and random forest create a robust system capable of extracting the road in very changeable situations.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.26 no.7
• /
• pp.844-850
• /
• 2016

This study pertains to the extraction of the road noise component of signals from a vehicle's interior noise via the traditional frequency domain and time domain system identification methods. For road noise extraction based on the frequency domain system identification method, the appropriate matrix inversion strategy is investigated and causal and non-causal impulse response filters are compared. Furthermore, appropriate data lengths for the frequency domain system identification method are investigated. In addition to the traditional road noise extraction methods based on frequency domain system identification, a new approach to extract road noise via the time domain system identification method based on a parametric input-output model is proposed and investigated in the present study. In this approach, instead of constructing a higher order model for the full-band road noise, input and output signals are processed in the subband domain and lower order parametric models optimal to each subband are determined. These parametric models are used to extract road noises in each subband; the full band road noise is then reconstructed from the subband road noises. This study shows that both the methods in the frequency domain and the time domain successfully extract the road noise from the vehicle's interior noise.

• Korean Journal of Remote Sensing
• /
• v.19 no.5
• /
• pp.393-402
• /
• 2003

In times of the civil uses of commercialized high-resolution satellite imagery, applications of remote sensing have been widely extended to the new fields or the problem solving beyond traditional application domains. Transportation application of this sensor data, related to the automatic or semiautomatic road extraction, is regarded as one of the important issues in uses of remote sensing imagery. Related to these trends, this study focuses on automatic road extraction using Gradient Direction Profile Algorithm (GDPA) scheme, with IKONOS panchromatic imagery having 1 meter resolution. For this, the GDPA scheme and its main modules were reviewed with processing steps and implemented as a prototype software. Using the extracted bi-level image and ground truth coming from actual GIS layer, overall accuracy evaluation and ranking error-assessment were performed. As the processed results, road information can be automatically extracted; by the way, it is pointed out that some user-defined variables should be carefully determined in using high-resolution satellite imagery in the dense or low contrast areas. While, the GDPA method needs additional processing, because direct results using this method do not produce high overall accuracy or ranking value. The main advantage of the GDPA scheme on road features extraction can be noted as its performance and further applicability. This experiment study can be extended into practical application fields related to remote sensing.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.525-527
• /
• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.5
• /
• pp.28-34
• /
• 2007

A histogram-based road border line extractor is proposed for an efficient road extraction from the high-resolution satellite imagery. The road border lines are extracted from an edge strength map based on the directional histogram difference between the road and the non-road region. The straight and the curved roads are extracted hierarchically from the edge strength map of the original image and the segmented road cluster images, and the road network is constructed based on the connectivity. Unlike the conventional approaches based on the spectral similarity, the proposed road extraction method is more robust to noise because it extracts roads based on the histogram, and is able to extract both the location and the width of roads. In addition, the proposed method can extract roads with various spectral characteristics by identifying the road clusters automatically. Experimental results on IKONOS multi-spectral satellite imagery with high spatial resolution show that the proposed method can extract the straight and the curved roads as well as the accurate road border lines.

• Korean Journal of Remote Sensing
• /
• v.27 no.4
• /
• pp.421-433
• /
• 2011

The importance of acquisition of road information has recently been increased with a rapid growth of spatial-related services such as urban information system and location based service. This paper proposes an automatic road extraction method using object-based approach which was issued alternative of pixel-based method recently. Firstly, the spatial objects were created by MSRS(Modified Seeded Region Growing) method, and then the key road objects were extracted by using properties of objects such as their shape feature information and adjacency. The omitted road objects were also traced considering spatial correlation between extracted road and their neighboring objects. In the end, the final road region was extracted by connecting discontinuous road sections and improving road surfaces through their geometric properties. To assess the proposed method, quantitative analysis was carried out. From the experiments, the proposed method generally showed high road detection accuracy and had a great potential for the road extraction from high resolution satellite images.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.837-840
• /
• 1991

This paper describes an implementation of road network extraction algorithms for satellite images. We propose a new road network extraction algorithm which uses magnitude and direction information of edges. The results of applying the proposed algorithm to satellite images are presented and compared with those of other algorithms.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.405-410
• /
• 1999

Topographic maps can be made and updated with satellite images, but it requires many human interactions that are inefficient and costly. Therefore, the automatizing of the road extraction procedures could increase efficiency in terms of time and cost. Although methods of extracting roads, railroads and rivers from satellite images have been developed in many studies, studies on the road extraction from satellite images of urbanized area are still not relevant, because many artificial components In the city makes the delineation of the roads difficult. So, to extract roads from high resolution satellite images of urbanized area, this study has proposed the combined use of wavelet transform and multi-resolution analysis. In consequence, this study verifies that it is possible to automatize the road extraction from satellite images of urbanized area. And to realize the automatization more completely, various algorithms need to be developed.