Road network extraction using multi-layered filtering and tensor voting from aerial images

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.

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

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2007.906107 ◽

2007 ◽

Vol 45 (12) ◽

pp. 4144-4157 ◽

Cited By ~ 187

Author(s):

Jiuxiang Hu ◽

Anshuman Razdan ◽

John C. Femiani ◽

Ming Cui ◽

Peter Wonka

Keyword(s):

Road Network ◽

Aerial Images ◽

Intersection Detection ◽

Road Network Extraction

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-925-2020 ◽

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.

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Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Remote Sensing ◽

10.3390/rs8080637 ◽

2016 ◽

Vol 8 (8) ◽

pp. 637 ◽

Cited By ~ 22

Author(s):

Mehdi Maboudi ◽

Jalal Amini ◽

Michael Hahn ◽

Mehdi Saati

Keyword(s):

Road Network ◽

Satellite Images ◽

Feature Integration ◽

Context Aware ◽

Tensor Voting ◽

Road Network Extraction ◽

Object Feature

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

Remote Sensing ◽

10.3390/rs11091012 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1012 ◽

Cited By ~ 4

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.

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A Multiscale Method for Road Network Extraction from High-Resolution SAR Images Based on Directional Decomposition and Regional Quality Evaluation

Remote Sensing ◽

10.3390/rs13081476 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1476

Author(s):

Wenjing He ◽

Hongjun Song ◽

Yuanyuan Yao ◽

Xinlin Jia

Keyword(s):

High Resolution ◽

Road Network ◽

Quality Evaluation ◽

Multiscale Method ◽

Tensor Voting ◽

Threshold Method ◽

Sar Images ◽

Practical Applications ◽

Road Network Extraction ◽

Disaster Assessment

Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.

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