Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform

Sghaier, Moslem Ouled; Lepage, Richard

doi:10.1109/jstars.2015.2449296

Cited by 86 publications

(52 citation statements)

References 37 publications

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“…In previous works, we successfully applied the SFS texture measurement to extract homogeneous areas such as road networks from optical images [31,32], and water bodies from SAR images [29,30]. This descriptor has shown very promising results applied to optical images characterized by the presence of various artifacts arising from the use of high spatial resolution and SAR images affected by speckle noise.…”

Section: Texture Extraction Using a Structural Feature Set (Sfs)mentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

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Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.

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Section: Texture Extraction Using a Structural Feature Set (Sfs)mentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

Self Cite

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“…The reference road maps are handdrawn according to the input image. Figure 8 illustrates the evaluation principle [47].…”

Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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“…This method is professional and can be used to collect abundant information but is of low efficiency and requires a large investment. (2) Remote sensing image extraction [2,3], which first obtains the latest high-resolution remote sensing images or aerial photographs of the target area. Then, the initial information is extracted from these images and a field survey is conducted to collect other attribute information.…”

Section: Literature Reviewmentioning

confidence: 99%

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

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Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.

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Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform

Cited by 86 publications

References 37 publications

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

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