scholarly journals A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Mohamad M. Awad
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Extraction Methods ◽  
Positional Accuracy ◽  
Closed Curves ◽  
New Model ◽  
Detection Techniques ◽  
The Road ◽  
High Resolution Satellite Images

Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the enhanced image, the application of the morphological operators, and finally the extraction of the road network. The new model is more accurate position wise and requires less effort and time compared to the traditional supervised and semi-supervised urban extraction methods such as simple edge detection techniques or manual digitization. Experiments conducted on high-resolution satellite images prove the high accuracy and the efficiency of the new model. The positional accuracy of the extracted road features compared to the manual digitized ones, the counted number of detected road segments, and the percentage of completely closed and partially closed curves prove the efficiency and accuracy of the new model.

scholarly journals ROAD NETWORK IDENTIFICATION AND EXTRACTION IN SATELLITE IMAGERY USING OTSU'S METHOD AND CONNECTED COMPONENT ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 91-98 ◽  
Author(s):  
P. Yadav ◽  
S. Agrawal
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Component Analysis ◽  
Morphological Operations ◽  
Connected Component ◽  
Road Detection ◽  
Connected Component Analysis ◽  
High Resolution Satellite Images ◽  
Otsu’S Method

<p><strong>Abstract.</strong> As the high resolution satellite images have become easily available, this has motivated researchers for searching advanced methods for object detection and extraction from satellite images. Roads are important curvilinear object as they are a used in urban planning, emergency response, route planning etc. Automatic road detection from satellite images has now become an important topic in photogrammetry with the advances in remote sensing technology. In this paper, a method for road detection and extraction of satellite images has been introduced. This method uses the concept of histogram equalization, Otsu's method of image segmentation, connected component analysis and morphological operations. The aim of this paper is to discover the potential of high resolution satellite images for detecting and extracting the road network in a robust manner.</p>

scholarly journals ROADS DATA CONFLATION USING UPDATE HIGH RESOLUTION SATELLITE IMAGES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 3-7
Author(s):  
A. Abdollahi ◽  
H. R. Riyahi Bakhtiari
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Geographic Information ◽  
Network Data ◽  
Geometric Accuracy ◽  
Traditional Methods ◽  
The Road ◽  
Sensing Technology ◽  
High Resolution Satellite Images ◽  
Map Data

Urbanization, industrialization and modernization are rapidly growing in developing countries. New industrial cities, with all the problems brought on by rapid population growth, need infrastructure to support the growth. This has led to the expansion and development of the road network. A great deal of road network data has made by using traditional methods in the past years. Over time, a large amount of descriptive information has assigned to these map data, but their geometric accuracy and precision is not appropriate to today’s need. In this regard, the improvement of the geometric accuracy of road network data by preserving the descriptive data attributed to them and updating of the existing geo databases is necessary. Due to the size and extent of the country, updating the road network maps using traditional methods is time consuming and costly. Conversely, using remote sensing technology and geographic information systems can reduce costs, save time and increase accuracy and speed. With increasing the availability of high resolution satellite imagery and geospatial datasets there is an urgent need to combine geographic information from overlapping sources to retain accurate data, minimize redundancy, and reconcile data conflicts. <br><br> In this research, an innovative method for a vector-to-imagery conflation by integrating several image-based and vector-based algorithms presented. The SVM method for image classification and Level Set method used to extract the road the different types of road intersections extracted from imagery using morphological operators. For matching the extracted points and to find the corresponding points, matching function which uses the nearest neighborhood method was applied. Finally, after identifying the matching points rubber-sheeting method used to align two datasets. Two residual and RMSE criteria used to evaluate accuracy. The results demonstrated excellent performance. The average root-mean-square error decreased from 11.8 to 4.1&amp;thinsp;m.

scholarly journals PERFORMANCE EVALUATION OF THREE DIFFERENT HIGH RESOLUTION SATELLITE IMAGES IN SEMI-AUTOMATIC URBAN ILLEGAL BUILDING DETECTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 505-514
Author(s):  
N. Khalilimoghadama ◽  
M. R. Delavar ◽  
P. Hanachi
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Building Detection ◽  
Detection Techniques ◽  
Efficient Detection ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Improved Performance ◽  
Under Construction ◽  
Mega Cities

The problem of overcrowding of mega cities has been bolded in recent years. To meet the need of housing this increased population, which is of great importance in mega cities, a huge number of buildings are constructed annually. With the ever-increasing trend of building constructions, we are faced with the growing trend of building infractions and illegal buildings (IBs). Acquiring multi-temporal satellite images and using change detection techniques is one of the proper methods of IB monitoring. Using the type of satellite images with different spatial and spectral resolutions has always been an issue in efficient detection of the building changes. In this research, three bi-temporal high-resolution satellite images of IRS-P5, GeoEye-1 and QuickBird sensors acquired from the west of metropolitan area of Tehran, capital of Iran, in addition to city maps and municipality property database were used to detect the under construction buildings with improved performance and accuracy. Furthermore, determining the employed bi-temporal satellite images to provide better performance and accuracy in the case of IB detection is the other purpose of this research. The Kappa coefficients of 70&amp;thinsp;%, 64&amp;thinsp;%, and 68&amp;thinsp;% were obtained for producing change image maps using GeoEye-1, IRS-P5, and QuickBird satellite images, respectively. In addition, the overall accuracies of 100&amp;thinsp;%, 6&amp;thinsp;%, and 83&amp;thinsp;% were achieved for IB detection using the satellite images, respectively. These accuracies substantiate the fact that the GeoEye-1 satellite images had the best performance among the employed images in producing change image map and detecting the IBs.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High
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