Automatic road network extraction using high resolution multi-temporal satellite images

2009 ◽  
Author(s):  
Vinay Pandit ◽  
Sudhir Gupta ◽  
K.S. Rajan
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Road Network Extraction ◽  
Multi Temporal

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 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 Extraction from High Resolution Multispectral Satellite Imagery Based on Object Oriented Techniques

2014 ◽  
Vol II-8 ◽  
pp. 107-110 ◽  
Author(s):  
M. Kumar ◽  
R. K. Singh ◽  
P. L. N. Raju ◽  
Y. V. N. Krishnamurthy
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Network ◽  
Object Oriented ◽  
Integrated Approach ◽  
Morphological Operations ◽  
Target Feature ◽  
Fuzzy Classifier ◽  
The Road ◽  
Road Network Extraction

High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resolution space imagery. The proposed approach begins with segmentation of the scene with Multi-resolution Object Oriented segmentation. This step focuses on exploiting both spatial and spectral information for the target feature extraction. The road regions are automatically identified using a soft fuzzy classifier based on a set of predefined membership functions. A number of shape descriptors are computed to reduce the misclassifications between road and other spectrally similar objects. The detected road segments are further refined using morphological operations to form final road network, which is then evaluated for its completeness, correctness and quality. The experiments were carried out of fused IKONOS 2 , Quick bird ,Worldview 2 Products with fused resolution’s ranging from 0.5m to 1 m. Results indicate that the proposed methodology is effective in extracting accurate road networks from high resolution imagery.

scholarly journals USING OF VHR SATELLITE IMAGES FOR ROAD NETWORK EXTRACTION IN EGYPT

2020 ◽  
Vol 48 (1) ◽  
pp. 20-31
Author(s):  
Beshoy Nady ◽  
Yasser Mostafa ◽  
Yousef A. Abbas ◽  
Mahmoud Enieb
Keyword(s):  
Road Network ◽  
Satellite Images ◽  
Road Network Extraction

scholarly journals Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

2019 ◽  
Vol 11 (21) ◽  
pp. 2499 ◽  
Author(s):  
Jiang Xin ◽  
Xinchang Zhang ◽  
Zhiqiang Zhang ◽  
Wu Fang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Intelligent Transportation Systems ◽  
Road Network ◽  
Semantic Segmentation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Road Network Extraction

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

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