Multiple Saliency Features Based Automatic Road Extraction from High‐Resolution Multispectral Satellite Images

Zhang, Jing; Lu, Chen; Wang, Chao

doi:10.1049/cje.2017.11.008

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…In some HRSI, an evident intensity difference is observed between the roads and the background. The K-means clustering algorithm can be used to segment an image into different clusters [72] [73], as displayed in Fig. 6.…”

Section: Methods Based On Segmentationmentioning

confidence: 99%

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Lian

Wang

Mustafa

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Road extraction from high-resolution remote sensing images (HRSI) is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem. This article provides a comprehensive review of these existing approaches. We classified the methods into heuristic and data-driven. The heuristic methods are the mainstream in the early years, and the data-driven methods based on deep learning have been quickly developed recently. With regard to the heuristic methods, the road feature model is firstly introduced, then, the classic extraction methods are reviewed in two sub-categories: semi-automatic and automatic. The principles, inspirations, advantages and disadvantages of these methods are described. In terms of the data-driven methods, the road extraction methods based on deep neural network, particularly those based on patched convolutional neural network, fully convolutional network and generative adversarial network are reviewed. We perform subjective comparisons between the methods inner each type. Furthermore, the quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data-driven methods. Finally, the conclusion and prospects are summarized.

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Section: Methods Based On Segmentationmentioning

confidence: 99%

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Lian

Wang

Mustafa

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, the method is unsuitable for extracting road class from lowresolution images with a spatial resolution below six meters. Some eminent shape features were used by Zhang, et al, (2018) for road class extraction from remote sensing imagery. They first extracted road edge using singular value decomposition method and then constructed road sections using k-mean clustering approach.…”

Section: Related Workmentioning

confidence: 99%

“…Road lengths are lengthy and generally longer than those of street blocks and buildings, while road width is usually a few pixels in remote sensing imagery (Sujatha & Selvathi, 2015). Therefore, precise road network extraction from very high-resolution remotely sensed images is necessary for different kinds of urban applications, such as updating maps in geographic information system (Abdollahi, Pradhan, & Shukla, 2019), road navigation (Li, Jin, Fei, & Ma, 2014), land cover analysis (Zhang, Chen, Zhuo, Geng, & Wang, 2018), and transportation and traffic management (Liu, Wu, Wang, & Liu, 2015). However, owing to existing obstructions and noise in these images, such as contextual structures (shadows, vehicles, vegetation, and trees) and road-like features (such as car parking and railways), which have similar spectral and spatial characteristics and produce heterogeneous areas causing the incorrect segmentation of road parts, extracting road parts from remotely sensed imagery becomes a challenging task (Li, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images

Abdollahi

Pradhan

2021

Expert Systems with Applications

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Road networks are one of the main urban features. Therefore, road parts extraction from high-resolution remotely sensed imagery and updated road database are beneficial for many GIS applications. However, owing to the presence of various types of obstacles in the images, such as shadows, cars, and trees, with similar transparency and spectral values as road class, achieving accurate road extraction using different classification and segmentation methods is still difficult. This paper proposes an integrated method combining segmentation and classification methods with connected components analysis to extract road class from orthophoto images. The proposed technique is threefold. First, multiresolution segmentation method was applied to segment images. Then, the main classification methods, namely, decision trees (DT), k-nearest neighbors (KNN), and support vector machines (SVM), were implemented based on spectral, geometric, and textural information to classify the obtained results into two classes: road and non-road. Three main accuracy evaluation measures, such as recall, precision, and F1-score, were evaluated to determine the performance of the proposed method, with respective average values of 87.62%, 89.71%, and 88.61%, respectively, for DT; 86.61%, 88.17%, and 87.30%, respectively, for KNN; and 89.83%, 89.52%, and 89.67%, respectively, for SVM. Finally, connected components labelling was used to extract road component parts, and morphological operation was employed to delete non-road parts and noises and improve the performance. These results were also compared with other prior works, which confirmed that the integrated method is an effective road extraction technique.

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“…However, shadows, overlapping, interlacing, and shadowing in satellite images [5] make road segment extraction challenging [6]. The manual segmentation of roads is feasible based on careful examination of images, but such segmentation is costly, time-consuming, and prone to errors due to its tedious nature [7]. Thus, automatic means are necessary for accurately extracting road segments from highresolution remote sensing imagery [8].…”

Section: Introductionmentioning

confidence: 99%

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

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Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing highresolution remote sensing imagery. In this study, we propose a generative adversarial network (GAN)-based deep learning approach for road segmentation from high-resolution aerial imagery. In the generative part of the presented GAN approach, we use a modified UNet model (MUNet) to obtain a high-resolution segmentation map of the road network. In combination with simple pre-processing comprising edge-preserving filtering, the proposed approach offers a significant improvement in road network segmentation compared with prior approaches. In experiments conducted on the Massachusetts road image dataset, the proposed approach achieves 91.54% precision and 92.92% recall, which correspond to a Mathews correlation coefficient (MCC) of 91.13%, a Mean intersection over union (MIOU) of 87.43% and a F1-score of 92.20%. Comparisons demonstrate that the proposed GAN framework outperforms prior CNN-based approaches and is particularly effective in preserving edge information.

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Multiple Saliency Features Based Automatic Road Extraction from High‐Resolution Multispectral Satellite Images

Cited by 18 publications

References 17 publications

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images

Improving Road Semantic Segmentation Using Generative Adversarial Network

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