scholarly journals FPGA IMPLEMENTATION OF ROAD NETWORK EXTRACTION USING MORPHOLOGICAL OPERATOR

2016 ◽  
Vol 35 (2) ◽  
pp. 93 ◽  
Author(s):  
Sujatha Chinnathevar ◽  
Selvathi Dharmar
Keyword(s):  
Road Network ◽  
Hardware Implementation ◽  
Ground Truth ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Average Value ◽  
Road Map ◽  
Road Network Extraction ◽  
Morphological Operator

In the remote sensing analysis, automatic extraction of road network from satellite or aerial images is the most needed approach for efficient road database creation, refinement, and updating. Mathematical morphology is a tool for extracting the features of an image that are useful in the representation and description of region shape. Morphological operator plays a significant role in the extraction of road network from satellite images. Most of the image processing algorithms need to handle large amounts of data, high repeatability, and general software is relatively slow to implement, so the system cannot achieve real-time requirements. In this paper, field programmable gate array (FPGA) architecture designed for automatic extraction of road centerline using morphological operator is proposed. Based on simulation and implementation, results are discussed in terms of register transfer level (RTL) design, FPGA editor and resource estimation. For synthesis and implementation of the above architecture, Spartan 3 XC3S400TQ144-4 device is used. The hardware implementation results are compared with software implementation results. The performance of proposed method is evaluated by comparing the results with ground truth road map as reference data and performance measures such as completeness, correctness and quality are calculated. In the software imple-mentation, the average value of completeness, correctness, and quality of various images are 90%, 96%, and 87% respectively. In the hardware implementation, the average value of completeness, correctness, and quality of various images are 87%, 94%, and 85% respectively. These measures prove that the proposed work yields road network very closer to reference road map.

scholarly journals Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

2019 ◽  
Vol 11 (9) ◽  
pp. 1012 ◽  
Author(s):  
Prajowal Manandhar ◽  
Prashanth Reddy Marpu ◽  
Zeyar Aung ◽  
Farid Melgani
Keyword(s):  
Deep Learning ◽  
United Arab Emirates ◽  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
The Road ◽  
Second Stage ◽  
Road Map ◽  
Road Network Extraction ◽  
Multi Agent

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.

scholarly journals ROAD EXTRACTION AND VECTORIZATION FROM AERIAL IMAGE DATA

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1323-1327
Author(s):  
L. Zhu ◽  
Y. Li ◽  
H. Shimamura
Keyword(s):  
Road Network ◽  
Image Data ◽  
Road Surface ◽  
Aerial Image ◽  
Surface Model ◽  
Center Line ◽  
Road Extraction ◽  
The Road ◽  
Road Map ◽  
Road Network Extraction

Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was proposed to address the issues of tedious steps on segmentation, recognition and grouping, using the digital surface model (DSM) only. The key feature of the proposed approach was the cross validation of the road basic elements, which was applied all the way through the entire procedure of road extraction. In the second stage, the regularized road map was produced where center line and side lines subject to parallel and even layout rules. It gives more accurate and reliable map by utilizing both the height information of the DSM and the color information of the ortho image. Road surface was extracted from the image by region growing. Then, a regularized center line was modeled by linear regression using all the pixels of the road surface. The road width was estimated and two road side lines were modeled as the straight lines parallel with the center line. Finally, the road model was built up in terms of vectorized points and lines. The experimental results showed that the proposed approach performed satisfactorily in our test site.

Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints

2007 ◽  
Vol 45 (12) ◽  
pp. 4144-4157 ◽  
Author(s):  
Jiuxiang Hu ◽  
Anshuman Razdan ◽  
John C. Femiani ◽  
Ming Cui ◽  
Peter Wonka
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Intersection Detection ◽  
Road Network Extraction

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.

Korelasi kreativitas dan motivasi kerja guru terhadap peningkatan mutu pembelajaran

2016 ◽  
Vol 1 (1) ◽  
pp. 51
Author(s):  
Muh Nashiruddin ◽  
Anharurrohman El Muhammadi
Keyword(s):  
Religious Education ◽  
Work Motivation ◽  
Sampling Technique ◽  
Islamic Education ◽  
Learning Improvement ◽  
Average Value ◽  
Teacher Creativity ◽  
Teacher Work ◽  
And Behavior

Penelitian ini bertujuan untuk mengetahui korelasi antara: 1) kreativitas guru PAI terhadap peningkatan mutu pembelajaran Pendidikan Agama dan Budi Pekerti , 2) motivasi kerja guru PAI terhadap peningkatan mutu pembelajaran Pendidikan Agama dan Budi Pekerti , dan 3) kreativitas dan motivasi kerja guru terhadap peningkatan mutu pembelajaran Pendidikan Agama dan Budi. Penelitian ini menggunakan metode deskripsi korelasional dengan melibatkan 33 orang sampel yang dipilih secara dengan metode sensus. Teknik pengumpulan data dilakukan dengan angket/kuesioner. Teknikanalisis data diawali dengan uji prasyarat yaitu uji normalitas dan linieritas. Uji hipotesis menggunakanuji regresi sederhana danuji regresi ganda. Hasil penelitian menunjukkan ; 1) Kreativitas guru berpengaruh terhadap peningkatan mutu pembelajaran Pendidikan Agama Islam dan Budi Pekerti. 2) motivasi kerja guru berpengaruh terhadap peningkatan mutu pembelajaran Pendidikan Agama Islam dan Budi Pekerti. 3) kreativitas guru dan motivasi kerja guru secara bersama-sama atau simultan berpengaruh terhadap mutu pembelajaran. Hasil analisis juga menunjukkan bahwa rata-rata nilai dari ketiga variabel tersebut hanya dapat dimasukkan dalam kategori sedang, sehingga untuk meningkatkan kinerja guru dalam mengajar perlu diperhatikan faktor-faktor lain seperti: gaji, jaminan kerja, jaminan hari tua, penghargaan atas prestasi kerja, dan sebagainya. Kata kunci: kreativitas, motivasi, mutupembelajaran Abstract [The Relationship Between Teachers’ Creativity and Motivation Toward Learning Improvement]. This research aim at determine the correlation between the creativity of teachers on the improvement of learning quality of religious education and behavior, the work motivation of teachers on the improvement of learning quality of religious education and behavior, and the creativity and the work motivation of eachers on the improvement of learning quality of religious education and behavior. This research employed a method of correlation description with 33 Islamic education teacher were selected as sample by means of census sampling. Technique of collecting data employed questionnaire. Technique of data analysis began with prerequisite of normality and linearity. Hypothesis used test of simple regression and double regression.The study revealed that teacher creativity affects on the improvement of learning quality of religious education and behavior, as well as teacher work motivation affects on the improvement of learning quality of religious education and behavior. Futhermore, analysis show that teacher creativity as well as teacher work motivation affects simultaneously on the learning quality of Islamic Education. In conclusion, the average value of those three variables may be only included into medium category. Thus, to increase teacher performance, it needs other factors, such as; salary, job guarantee, pension, appreciation of work achievement, etc. Keywords: Creativity, Motivation, Learning Quality

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

RESEARCH ON AUTOMATIC EXTRACTION OF DEFOCUS IMAGES IN UAV AERIAL IMAGES

2019 ◽  
Vol 75 (2) ◽  
pp. I_106-I_113
Author(s):  
Etsuji KITAGAWA ◽  
Ryo KATO ◽  
Satoshi ABIKO ◽  
Takumi TSUMURA ◽  
Yusuke NAKATANI
Keyword(s):  
Aerial Images ◽  
Automatic Extraction

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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