scholarly journals A Multiscale Method for Road Network Extraction from High-Resolution SAR Images Based on Directional Decomposition and Regional Quality Evaluation

2021 ◽  
Vol 13 (8) ◽  
pp. 1476
Author(s):  
Wenjing He ◽  
Hongjun Song ◽  
Yuanyuan Yao ◽  
Xinlin Jia
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Quality Evaluation ◽  
Multiscale Method ◽  
Tensor Voting ◽  
Threshold Method ◽  
Sar Images ◽  
Practical Applications ◽  
Road Network Extraction ◽  
Disaster Assessment

Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.

scholarly journals Road Network Extraction from High Resolution Multispectral Satellite Imagery Based on Object Oriented Techniques

2014 ◽  
Vol II-8 ◽  
pp. 107-110 ◽  
Author(s):  
M. Kumar ◽  
R. K. Singh ◽  
P. L. N. Raju ◽  
Y. V. N. Krishnamurthy
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Network ◽  
Object Oriented ◽  
Integrated Approach ◽  
Morphological Operations ◽  
Target Feature ◽  
Fuzzy Classifier ◽  
The Road ◽  
Road Network Extraction

High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resolution space imagery. The proposed approach begins with segmentation of the scene with Multi-resolution Object Oriented segmentation. This step focuses on exploiting both spatial and spectral information for the target feature extraction. The road regions are automatically identified using a soft fuzzy classifier based on a set of predefined membership functions. A number of shape descriptors are computed to reduce the misclassifications between road and other spectrally similar objects. The detected road segments are further refined using morphological operations to form final road network, which is then evaluated for its completeness, correctness and quality. The experiments were carried out of fused IKONOS 2 , Quick bird ,Worldview 2 Products with fused resolution’s ranging from 0.5m to 1 m. Results indicate that the proposed methodology is effective in extracting accurate road networks from high resolution imagery.

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

2019 ◽  
Vol 11 (21) ◽  
pp. 2499 ◽  
Author(s):  
Jiang Xin ◽  
Xinchang Zhang ◽  
Zhiqiang Zhang ◽  
Wu Fang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Intelligent Transportation Systems ◽  
Road Network ◽  
Semantic Segmentation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Road Network Extraction

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.

Detection of linear features in SAR images: application to road network extraction

1998 ◽  
Vol 36 (2) ◽  
pp. 434-453 ◽  
Author(s):  
F. Tupin ◽  
H. Maitre ◽  
J.-F. Mangin ◽  
J.-M. Nicolas ◽  
E. Pechersky
Keyword(s):  
Road Network ◽  
Sar Images ◽  
Linear Features ◽  
Road Network Extraction
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