• 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.

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.350-353
• /
• 2009

본 논문에서는 국토모니터링 기술의 한 부분으로서 도로 지역에 대한 효율적인 실시간 교통모니터링을 위해 도로상의 차량 정보를 LiDAR 데이터로부터 취득하는 과정을 실험하였다. 도로영역의 데이터를 추출하기 위해서 좌표 변환된 수치지도와 LiDAR 데이터를 이용하였고, 국지적 임계치 필터링을 사용하여 추출된 도로영역의 데이터를 차량과 도로의 자료로 분리시키는 작업을 수행하였으며, 추출된 차량의 포인트들을 이용하여 차량을 표현할 수 있는 기본 속성값을 추출하였다. 마지막으로, 분리된 차량의 포인트에 대해서 MDC(Minimum Distance Classification) 클러스터링를 이용하여 차량의 종류를 분류하였다. 결과적으로 본 연구를 통하여 차량인식과 차량의 종류에 대한 분류를 수행할 수 있음을 확인하였다.

• 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.

• 한국지형공간정보학회:학술대회논문집
• /
• 2003.09a
• /
• pp.163-168
• /
• 2003

급속한 도시화와 교통 수요의 증가로 인해 각종 도로 정보가 빠르게 변화하고 있어 이에 대한 정보들의 추출, 수정 또는 보완의 중요성이 커지고 있다. 도로 정보 및 도로 파손 상태 정보를 이용하면 도로를 효율적이고 경제적으로 유자 관리할 수 있고, 수치지도 및 GIS 분석의 입력자료로서 사용될 수 있다. 본 연구에서는 분당지역의 항공사진을 이용하여 반자동으로 도로를 추출하였다. 항공사진에 탑햇 필터(top-hat filter)를 적용하고 적절한 임계값을 설정하여 후보 개체를 선택하고 모양 유사성을 고려하여 차로분리선만을 선택함으로써 효율적으로 차로분리선을 추출하였다. 추출된 차로분리선을 연결하고 중심선을 구함으로써 도심지역의 도로를 추출할 수 있었다.

• Proceedings of the KSRS Conference
• /
• 2001.03a
• /
• pp.3-8
• /
• 2001

인공위성 영상이나 항공영상에서 도로를 추출하는 시스템을 구현하는 연구는 지난 20년 동안 많이 진행되어 왔다. 본 논문은 해상도가 1m-2m 정도되는 도시영상에서 직각으로 구성된 도로를 추출하는 시스템의 구현에 관한 것이다. 도시영상에서는 도시내의 가로수나 건물들이 도로를 가리게 되고, 또한 높은 건물의 그림자에 의하여 도로의 많은 부분이 가려지게 되는데, 이러한 경우에 고로를 추출할 수 있는 기법에 대하여 기술한다. 또한 도로상의 중앙 분리선이나 차선 분리선은 건물의 외곽선에 비하여 매우 약하게 나타나므로 영상에서 상대적으로 약하게 나타나는 이러한 도로의 표식을 검출하여 도로를 추출하는 기법에 대하여서도 기술한다.

• Journal of Digital Convergence
• /
• v.12 no.1
• /
• pp.359-364
• /
• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.75-81
• /
• 2012

Road identification is the important task for extracting a road region from the high-resolution satellite images, when the road candidates is extracted by the pre-processing tasks using a binarization, noise removal, and color processing. Therefore, we propose a noble approach for identifying a road using the orientation-selective spatial filters, which is motivated by a computational model of neuron cells found in the primary visual cortex. In our approach, after the neuron cell typed spatial filters is applied to the isolated connected-labeling road candidate regions, proposed method identifies the region of perceiving the strong orientation feature with the real road region. To evaluate the effectiveness of the proposed method, the accuracy&error ratio in the confusion matrix was measured from road candidates including road and non-road class. As a result, the proposed method shows the more than 92% accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.2
• /
• pp.289-297
• /
• 2009

In the paper, we propose the methodology to extract training dataset automatically for supervised classification of road networks. For the preprocessing, we co-register the airborne photos, LIDAR data and large-scale digital maps and then, create orthophotos and intensity images. By overlaying the large-scale digital maps onto generated images, we can extract the initial training dataset for the supervised classification of road networks. However, the initial training information is distorted because there are errors propagated from registration process and, also, there are generally various objects in the road networks such as asphalt, road marks, vegetation, cars and so on. As such, to generate the training information only for the road surface, we apply the Expectation Maximization technique and finally, extract the training dataset of the road surface. For the accuracy test, we compare the training dataset with manually extracted ones. Through the statistical tests, we can identify that the developed method is valid.

• Korean Journal of Remote Sensing
• /
• v.22 no.2
• /
• pp.123-129
• /
• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.2
• /
• pp.165-173
• /
• 2008

This study aims at generating automatically three dimensional geometric models of roads using LIDAR data and a digital map. The main processes in the proposed method are (1) generating a polygon encompassing a road region using a road layer from the digital map, (2) extracting LIDAR points within the road region using the polygon, (3) organizing the points into surface patches and grouping the patches into surface clusters, (4) searching the road surface clusters and generating the surface model from the points linked to the clusters, (5) refining the boundary using a digital map. By applying the proposed method to real data, we successfully generated the linear and surface information of the roads.