A Novel Method for Exactly Locating Road Intersections in Aerial Images

2009 ◽  
Author(s):  
Cheng-Fei Zhu ◽  
Shu-Xiao Li ◽  
Hong-Xing Chang ◽  
Yi-Ping Yang ◽  
Jiang Guo
Keyword(s):  
Aerial Images ◽  
Road Intersections ◽  
Novel Method

scholarly journals RADIOMETRIC AND GEOMETRIC ACCURACY ANALYSIS OF RASAT PAN IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 349-353
Author(s):  
S. Kocaman ◽  
I. Yalcin ◽  
M. Guler
Keyword(s):  
Accuracy Assessment ◽  
Aerial Images ◽  
Image Pair ◽  
Geometric Accuracy ◽  
Ground Control Points ◽  
Space Agency ◽  
Road Intersections ◽  
Geometric Quality ◽  
Quality Assessments

RASAT is the second Turkish Earth Observation satellite which was launched in 2011. It operates with pushbroom principle and acquires panchromatic and MS images with 7.5 m and 15 m resolutions, respectively. The swath width of the sensor is 30 km. The main aim of this study is to analyse the radiometric and geometric quality of RASAT images. A systematic validation approach for the RASAT imagery and its products is being applied. RASAT image pair acquired over Kesan city in Edirne province of Turkey are used for the investigations. The raw RASAT data (L0) are processed by Turkish Space Agency (TUBITAK-UZAY) to produce higher level image products. The image products include radiometrically processed (L1), georeferenced (L2) and orthorectified (L3) data, as well as pansharpened images. The image quality assessments include visual inspections, noise, MTF and histogram analyses. The geometric accuracy assessment results are only preliminary and the assessment is performed using the raw images. The geometric accuracy potential is investigated using 3D ground control points extracted from road intersections, which were measured manually in stereo from aerial images with 20 cm resolution and accuracy. The initial results of the study, which were performed using one RASAT panchromatic image pair, are presented in this paper.

scholarly journals Management of Delay Optimization at Intersections

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3087 ◽  
Author(s):  
Łukasz Kuźmiński ◽  
Piotr Maśloch ◽  
Marek Bazan ◽  
Tomasz Janiczek ◽  
Krzysztof Halawa ◽  
...  
Keyword(s):  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Traffic Lights ◽  
Intelligent Transport ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Road Intersections ◽  
Novel Method ◽  
Vehicle To Infrastructure

Congestion extends the time of the journey for both people and goods. Therefore, transport solutions should be optimized. Management scientists and technical scientists worked together in order to develop a proprietary solution to increase efficiency in terms of productivity improvements for intelligent transport systems. The most fundamental functions of management have been paired with a detailed analysis of city traffic. The authors developed a method for determining the order of vehicles at traffic lights and connected it with vehicle-to-vehicle communication and GPS signals. As a result, a novel method to increase the throughput of intersections is presented. This solution generates a sound signal in order to inform the driver that the preceding car has started moving forward. The proposed solution leads to the shortening of the reaction time of the drivers waiting in a queue. This situation is most common at red lights. Consequently, the traffic simulation shows that the discharge of queues at traffic lights may be quicker by up to 13.5%. Notably, that proposed solution does not require any modification of the infrastructure as well as any additional devices for vehicle-to-infrastructure communication at the road intersections. To conclude, proper implementation of the proposed solution will certainly contribute to efficiency improvements within intelligent transport systems, with the potential to reduce traffic jams.

scholarly journals AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 925-932
Author(s):  
Y. Wei ◽  
X. Hu ◽  
M. Zhang ◽  
Y. Xu
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Tensor Voting ◽  
Road Extraction ◽  
The Road ◽  
Road Width ◽  
Road Intersections ◽  
On The Road ◽  
Segmentation Problem

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.

scholarly journals Sequentially Delineation of Rooftops with Holes from VHR Aerial Images Using a Convolutional Recurrent Neural Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4271
Author(s):  
Wei Huang ◽  
Zeping Liu ◽  
Hong Tang ◽  
Jiayi Ge
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Irregular Shapes ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Novel Method ◽  
Statistical Indicators ◽  
Instance Segmentation

Semantic and instance segmentation methods are commonly used to build extraction from high-resolution images. The semantic segmentation method involves assigning a class label to each pixel in the image, thus ignoring the geometry of the building rooftop, which results in irregular shapes of the rooftop edges. As for instance segmentation, there is a strong assumption within this method that there exists only one outline polygon along the rooftop boundary. In this paper, we present a novel method to sequentially delineate exterior and interior contours of rooftops with holes from VHR aerial images, where most of the buildings have holes, by integrating semantic segmentation and polygon delineation. Specifically, semantic segmentation from the Mask R-CNN is used as a prior for hole detection. Then, the holes are used as objects for generating the internal contours of the rooftop. The external and internal contours of the rooftop are inferred separately using a convolutional recurrent neural network. Experimental results showed that the proposed method can effectively delineate the rooftops with both one and multiple polygons and outperform state-of-the-art methods in terms of the visual results and six statistical indicators, including IoU, OA, F1, BoundF, RE and Hd.

scholarly journals Multimodel Deep Learning for Person Detection in Aerial Images

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1459
Author(s):  
Mirela Kundid Vasić ◽  
Vladan Papić
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Contextual Information ◽  
Geographical Area ◽  
Aerial Images ◽  
Person Detection ◽  
Current State ◽  
Aerial Vehicle ◽  
Novel Method ◽  
Better Than

In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.

scholarly journals A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching

2020 ◽  
Vol 12 (3) ◽  
pp. 465 ◽  
Author(s):  
Jae-Hyun Park ◽  
Woo-Jeoung Nam ◽  
Seong-Whan Lee
Keyword(s):  
Image Matching ◽  
Google Earth ◽  
Aerial Images ◽  
Aerial Image ◽  
Geometric Transformation ◽  
Target Image ◽  
Qualitative And Quantitative ◽  
Deep Network ◽  
Quantitative Results ◽  
Novel Method

In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.

scholarly journals RADIOMETRIC AND GEOMETRIC ACCURACY ANALYSIS OF RASAT PAN IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 349-353
Author(s):  
S. Kocaman ◽  
I. Yalcin ◽  
M. Guler
Keyword(s):  
Accuracy Assessment ◽  
Aerial Images ◽  
Image Pair ◽  
Geometric Accuracy ◽  
Ground Control Points ◽  
Space Agency ◽  
Road Intersections ◽  
Geometric Quality ◽  
Quality Assessments

RASAT is the second Turkish Earth Observation satellite which was launched in 2011. It operates with pushbroom principle and acquires panchromatic and MS images with 7.5 m and 15 m resolutions, respectively. The swath width of the sensor is 30 km. The main aim of this study is to analyse the radiometric and geometric quality of RASAT images. A systematic validation approach for the RASAT imagery and its products is being applied. RASAT image pair acquired over Kesan city in Edirne province of Turkey are used for the investigations. The raw RASAT data (L0) are processed by Turkish Space Agency (TUBITAK-UZAY) to produce higher level image products. The image products include radiometrically processed (L1), georeferenced (L2) and orthorectified (L3) data, as well as pansharpened images. The image quality assessments include visual inspections, noise, MTF and histogram analyses. The geometric accuracy assessment results are only preliminary and the assessment is performed using the raw images. The geometric accuracy potential is investigated using 3D ground control points extracted from road intersections, which were measured manually in stereo from aerial images with 20 cm resolution and accuracy. The initial results of the study, which were performed using one RASAT panchromatic image pair, are presented in this paper.

Traffic Control Performance Evaluation at Road Intersections: A Case Study in the City of Changchun

2012 ◽  
Vol 209-211 ◽  
pp. 1021-1024
Author(s):  
Zhong Bo Liu ◽  
Zhao Sheng Yang
Keyword(s):  
Traffic Control ◽  
Control Mode ◽  
Control Performance ◽  
Sight Distance ◽  
Road Intersections ◽  
Novel Method ◽  
General Plane ◽  
And Performance ◽  
The City

According to the conditions of traffic flow and geometric characteristics of road intersections, a novel method is proposed to determine the sight distance triangle with the consideration of crossing intervals and traffic volume on the secondary road. Then the control mode is suggested for general plane intersections. In final, a simulation example in the city of Changchun is used to verify the effectiveness and performance of this traffic control mode model.

Sign in / Sign up

Export Citation Format

Share Document