Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images

Soni, Pramod Kumar; Rajpal, Navin; Mehta, Rajesh

doi:10.1007/s12524-019-01077-4

Cited by 27 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The shallow machine learning methods were also used in road extraction. Soni et al [15] proposed a supervised multilevel framework based on least squares support vector machine (LS-SVM), mathematical morphology and road shape features to extract road networks from remote sensing images. This method uses the LS-SVM method to segment the image into road and non-road regions, and then uses the morphological and shape features to extract non-road objects.…”

Section: Non-deep Learning Methodsmentioning

confidence: 99%

“…To solve the problem of limited quantity of data, some scholars applied generative adversarial networks (GANs) [24] to road extraction. Zhang et al [15] proposed a method based on the generative adversarial network, which showed better performance than other methods on the Massachusetts roads dataset. GAN can also be used to estimate the roads covered by trees or shadows.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Many automated methods have been used to extract road information such as spectral feature-based method, objectoriented method, shallow machine learning method, deep learning method and so on. Previous studies have classified existing road extraction algorithms from different point of views [13][14][15]. Considering the particularity of deep learning methods, in this article, we divided the existing methods into non-deep learning methods and deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

Shen

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road extraction in mountainous regions. Compared with road extraction in flat regions, road extraction in mountainous regions suffers more interference, due to shadows caused by mountains and road-like terrain. Furthermore, there are more practical problems involved when researching an entire region rather than at the sample level. To address the difficulties outlined regarding mountain road extraction, this paper takes Jiuzhaigou county in China as an example and studies road extraction in practical applications. Based on deep learning methods, we used a multistage optimization method to improve the extraction effect. First, we used the contrast limited adaptive histogram equalization (CLAHE) algorithm to attenuate the influence of mountain shadows and improve the quality of the image. Then the road was extracted by the improved DSDNet network. Finally, the terrain constraint method is used to reduce the false detection problem caused by the terrain factor, and after that the final road extraction result is obtained. To evaluate the effect of road extraction comprehensively, we used multiple data sources (i.e., points, raster and OpenStreetMap data) in different evaluation schemes to verify the accuracy of the road extraction results. The accuracy of our method for the three schemes was 0.8631, 0.8558 and 0.8801, which is higher than other methods have obtained. The results show that our method can effectively solve the interference of shadow and terrain encountered in road extraction over mountainous regions, significantly improving the effect of road extraction.

show abstract

Section: Non-deep Learning Methodsmentioning

confidence: 99%

Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

Shen

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, in general, deep learning methods require large amounts of data and a lot of time to train. Moreover, most of the existing traditional road extraction algorithms need many subsequent refinements to obtain the accurate road network, such as by extracting the length, the aspect ratio, the area and other geometric features, through one or more combinations to filter the non-road area to get the final road network (Miao et al, 2013;Liu et al, 2016;Cao et al, 2016;Soni et al, 2020). The subsequent processing steps achieve desirable results, but the addition of these steps affects the automation process of the road extraction.…”

Section: Al 2001mentioning

confidence: 99%

A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images

Guo

Zhipan

2020

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

View full text Add to dashboard Cite

Road extraction from the high resolution remote sensing image is significant for the land planning, vehicle navigation, etc. The existing road extraction methods normally need many pre-processing and subsequent optimization steps. Therefore, an automatic road centerline extraction method based on the self-supervised learning framework for high resolution remote sensing image is proposed. This proposed method does not need to manually select training samples and other optimization steps, such as the non-road area removing. Firstly, the positive sample selection method combining the spectral and shape features is proposed to extract the road sample. Then, the one-class classifier framework is introduced and the random forest positive unlabeled learning classifier is constructed to get the posterior probability of the pixel belonging to road. The shape feature and the posterior probability are combined to form the final road network in the object-oriented way. Finally, the road centerline is obtained through the tensor voting algorithm. In order to verify the effectiveness of the proposed algorithm, high resolution remote sensing images and benchmark datasets are used to do experiments. The indexes of the completeness ratio, the correctness ratio and the detection quality are used for the quantitative accuracy evaluation. Compared with the supervised, the unsupervised and the one-class classification road extraction algorithms, this proposed algorithm achieves high accuracy and efficiency.For the deep learning method comparison, the deep learning method performs well in most cases especially in the complex urban area. However, the deep learning method needs a large number of samples and a long training time, and our SSLF does not need the training samples.

show abstract

“…However, manually labeling roads in high resolution remote sensing image will cost a lot of time and effort [8,9]. To overcome this problem, many automatic methods, such as SVM [10,11] and CRF [12], have been proposed to extract road from high resolution remote sensing image. Compared with manual extraction method, automatic road extraction methods are more efficient and economical.With the development of deep learning, convolutional neural networks (CNNs) not only have been widely and successfully applied in semantic segmentation [13][14][15], but also play a key role in road extraction [16,17].…”

mentioning

confidence: 99%

Road extraction from high resolution remote sensing image via a deep residual and pyramid pooling network

Han

Jia

2021

IET Image Processing

View full text Add to dashboard Cite

The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. However, the existing approaches lack suitable strategy to utilize multiple views road features for road extraction, which fails to extract road with smooth appearance and accurate boundary under complex scenes. To address this problem, the authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image. The DRPPNet consists of three parts: deep residual network (DResNet), pyramid pooling module (PPM) and deep decoder (DD). Specially, the DResNet uses several residual blocks to extract deep road features from input images, which can enhance learning ability of DRPPNet and avoid gradient vanish. Then, PPM is proposed to fuse road features from multiple views and it aims to address disadvantage of single view feature. Finally, the DD is used to recover size of feature maps to input size. Extensive experiments on two challenging road datasets demonstrate that proposed method outperforms the state-of-the-art methods greatly on performance of road extraction task. INTRODUCTIONThe road is the backbone and essential infrastructure in many domains, such as urban planning, geographic information system updating and traffic navigation [1][2][3][4][5][6][7]. Recently, with the rapid development of satellites, high resolution remote sensing image can be easily accessed and contains road information in more detail, which provides a promising way to extract road. However, manually labeling roads in high resolution remote sensing image will cost a lot of time and effort [8,9]. To overcome this problem, many automatic methods, such as SVM [10,11] and CRF [12], have been proposed to extract road from high resolution remote sensing image. Compared with manual extraction method, automatic road extraction methods are more efficient and economical.With the development of deep learning, convolutional neural networks (CNNs) not only have been widely and successfully applied in semantic segmentation [13][14][15], but also play a key role in road extraction [16,17]. For example, [18][19][20] first used CNN to extract road from high resolution remote sensingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images

Cited by 27 publications

References 29 publications

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images

Road extraction from high resolution remote sensing image via a deep residual and pyramid pooling network

Contact Info

Product

Resources

About