Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Zhou, Tingting; Sun, Chenglin; Fu, Haoyu

doi:10.3390/rs11010079

Cited by 35 publications

(18 citation statements)

References 35 publications

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“…With the development of remote sensing technology and the advancement of remote sensing data processing methods, high temporal and spatial resolution, remote sensing data can provide high-precision ground information and permit the large-scale monitoring of roads. Remote sensing image data has quickly become the primary data source for the automatic extraction of road networks [2]. Automating road extraction plays a vital role in dynamic spatial development.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…From each dataset, we choose four images at the same locations for results display. We test our method performance in road segmentation, and compare it with the other two state-of-theart methods [15], [40], hereafter referred to as "Zhou 2019" and "Miao 2019", respectively. In our proposed method, the initial road line is buffered with width 4 as the initial area, time step ∆t = 2, μ =0.01, λ = 6, α = -3, K = 1000 in all the three experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Miao et al [40] exploited a level set method to compute the road area based on the Hue-Saturation-Value color space in the image and the centerline is extracted through the geodesic method. Zhou et al [15] proposed an automatic process that extracts road network. The road area was segmented by statistical region merging (SRM) firstly.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction Based on Level Set Approach From Very High-Resolution Images With Volunteered Geographic Information

et al. 2020

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The road network is of great importance to the modern traffic system, which changes frequently as its high speed of development. Simultaneously, a growing number of satellites provide a considerable number of remote sensing images that can be used for road extraction. However, most existing methods cannot get accurate results in the high-resolution remote sensing, since the complex background like the shadows and occlusions. Consequently, we proposed a method utilizing the volunteered geographic information data to extract the road from satellite images. At first, from Mapbox platform, the Volunteered Geographic Information data, OpenStreetMap vector tiles are collected to guide the downloading of corresponding road satellite images. Then a level set method is used to extract the road areas from the satellite images supported by the road vector. After that, a superpixel method is applied to improve the road areas by fusing the superpixels with the previous results. Experiments are performed on the satellite images from Mapbox, Google, and Yahoo in the Tianjin Port district on accuracy and efficiency. Experimental results demonstrate that our approach is more accurate and of higher performance than the other two stateof-the-art methods.

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“…Road extraction from high-resolution images is a basic application of remote sensing, which has attracted the attention of both academics and industry for a long time [27,28]. With the development of deep learning and contribution of specialized datasets from the remote sensing community, convolutional neural networks (CNNs) have been broadly used as alternatives to traditional methods for visual recognition tasks in remote sensing, including building detection [29], road segmentation [30][31][32], and topological map generation [33].…”

Section: Block Road Probability Map Generation By D-linknetmentioning

confidence: 99%

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

Yuan¹,

Yang

Li³

et al. 2020

Sensors

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Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.

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Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Cited by 35 publications

References 35 publications

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

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

Road Extraction Based on Level Set Approach From Very High-Resolution Images With Volunteered Geographic Information

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

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