Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

Liu, Jian; Qin, Qiming; Li, Jun; Liu, Yunpeng

doi:10.3390/ijgi6100314

Cited by 38 publications

(33 citation statements)

References 28 publications

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“…In traditional methods, finite element models designed by hand are used to enhance road connectivity by combining context and prior information, such as in high-order conditional random fields (CRF) [14] and junction-point processes [15]. Liu et al [16] proposed a road extraction method based on remote sensing images and geometric feature inference combined with a knowledge base of rural road geometry to try to solve the extraction problem for rural roads, characterized by diverse materials, large curvature change, and serious obscuration. Song and Civco [17] proposed a method to detect road regions using shape index features and support vector machines (SVM).…”

Section: Related Workmentioning

confidence: 99%

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

Miao

Zhou

Peng

2019

IJGI

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Road extraction is a unique and difficult problem in the field of semantic segmentation because roads have attributes such as slenderness, long span, complexity, and topological connectivity, etc. Therefore, we propose a novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling. HsgNet, taking the efficient LinkNet as its basic architecture, embeds a Middle Block between the Encoder and Decoder. The Middle Block learns to preserve global-context semantic information, long-distance spatial information and relationships, and different feature channels’ information and dependencies. It is different from other road segmentation methods which lose spatial information, such as those using dilated convolution and multiscale feature fusion to record local-context semantic information. The Middle Block consists of three important steps: (1) forming a feature resource pool to gather high-order global spatial information; (2) selecting a feature weight distribution, enabling each pixel position to obtain complementary features according to its own needs; and (3) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature. We compared multiple road extraction methods on two open datasets, SpaceNet and DeepGlobe. The results show that compared to the efficient road extraction model D-LinkNet, our model has fewer parameters and better performance: we achieved higher mean intersection over union (71.1%), and the model parameters were reduced in number by about 1/4.

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Section: Related Workmentioning

confidence: 99%

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

Miao

Zhou

Peng

2019

IJGI

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“…Because roads in the VHR images have specific structural characteristics like elongated shapes and large curvatures [3,10], the structural feature is useful for road extraction from the VHR images. The metric of density is used to describe structural characteristics of objects:…”

Section: Random Forest Classificationmentioning

confidence: 99%

“…The spatial resolution of the remote sensing images needs to be sufficiently high to allow road recognition [9]. Roads in the very-high-resolution (VHR) images are locally homogeneous and have elongated shapes with specific width [3,10]. Given the above characteristics, some studies designed the detectors of points and lines to extract road networks.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Hu et al [11] designed a spoke wheel operator for quantifying angular texture feature of pixels and detecting road pixels. Liu et al [10] first detected road edges from remote sensing data to extract the road networks. Shi et al [12] detected road segments by the template matching method and then generated a connected road network.…”

Section: Introductionmentioning

confidence: 99%

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Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Zhang

Sun

et al. 2018

Remote Sensing

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The road networks provide key information for a broad range of applications such as urban planning, urban management, and navigation. The fast-developing technology of remote sensing that acquires high-resolution observational data of the land surface offers opportunities for automatic extraction of road networks. However, the road networks extracted from remote sensing images are likely affected by shadows and trees, making the road map irregular and inaccurate. This research aims to improve the extraction of road centerlines using both very-high-resolution (VHR) aerial images and light detection and ranging (LiDAR) by accounting for road connectivity. The proposed method first applies the fractal net evolution approach (FNEA) to segment remote sensing images into image objects and then classifies image objects using the machine learning classifier, random forest. A post-processing approach based on the minimum area bounding rectangle (MABR) is proposed and a structure feature index is adopted to obtain the complete road networks. Finally, a multistep approach, that is, morphology thinning, Harris corner detection, and least square fitting (MHL) approach, is designed to accurately extract the road centerlines from the complex road networks. The proposed method is applied to three datasets, including the New York dataset obtained from the object identification dataset, the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D semantic labelling benchmark and Guangzhou dataset. Compared with two state-of-the-art methods, the proposed method can obtain the highest completeness, correctness, and quality for the three datasets. The experiment results show that the proposed method is an efficient solution for extracting road centerlines in complex scenes from VHR aerial images and light detection and ranging (LiDAR) data.

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“…Road is a kind of important basic geographic information, which undoubtedly occupies a pivotal position in urban land use and economic activities. Highly accurate and timely updating of road network information plays a very important role in traffic management, urban planning, automatic vehicle navigation, and emergency management [3]. Using the computer to extract road information from remote sensing images can not only update the road network information in time to achieve dynamic data acquisition, but also can be used for reference for the extraction of other linear features [4].…”

Section: Introductionmentioning

confidence: 99%

Research on road extraction of remote sensing image based on convolutional neural network

Jiang

2019

J Image Video Proc.

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Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management. With the development of remote sensing technology, the quality of high-resolution satellite images is improved and more easily obtained, which makes it possible to use remote sensing images to locate roads accurately. Therefore, it is an urgent problem to extract road information from remote sensing images. To solve this problem, a road extraction method based on convolutional neural network is proposed in this paper. Firstly, convolutional neural network is used to classify the high-resolution remote sensing images into two classes, which can distinguish the road from the non-road and extract the road information initially. Secondly, the convolutional neural network is optimized and improved from the training algorithm. Finally, because of the influence of natural scene factors such as house and tree shadow, the non-road noise still exists in the road results extracted by the optimized convolutional neural network method. Therefore, this paper uses wavelet packet method to filter these non-road noises, so as to accurately present the road information in remote sensing images. The simulation results show that the road information of remote sensing image can be preliminarily distinguished by convolutional neural network; the road information can be distinguished effectively by optimizing convolutional neural network; and the wavelet packet method can effectively remove noise interference. Therefore, the proposed road extraction method based on convolutional neural network has good road information extraction effect.

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Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

Cited by 38 publications

References 28 publications

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Research on road extraction of remote sensing image based on convolutional neural network

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