scholarly journals A Novel Road Extraction Algorithm for High Resolution Remote Sensing Images

2014 ◽  
Vol 8 (3) ◽  
pp. 1435-1443 ◽  
Author(s):  
Teng Xinpeng ◽  
Song Shunlin ◽  
Zhan Yongzhao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Extraction Algorithm

A Road Extraction Algorithm with Saliency Analysis in High-Resolution Remote Sensing Images

2015 ◽  
Vol 35 (s2) ◽  
pp. s210001
Author(s):  
王士一 Wang Shiyi ◽  
王双 Wang Shuang ◽  
张立保 Zhang Libao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Extraction Algorithm ◽  
Saliency Analysis

Road Extraction from High-Resolution Remote Sensing Images Based on Synthetical Characteristics

2013 ◽  
Vol 333-335 ◽  
pp. 828-831
Author(s):  
Yong Sheng Chen ◽  
Zhi Jia Hong ◽  
Qun He ◽  
Hong Bin Ma
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Geographic Information ◽  
Important Application ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Specific Process ◽  
Extraction Algorithm ◽  
Texture Characteristics

Road extraction is the recurring important application of high-resolution remote sensing images. In order to achieve the goal of road extraction, the various characteristics of geographic information of high-resolution remote sensing images as well as the application and models of road extraction are analyzed, then an effective way of extracting roads from high-resolution remote sensing images is found, and then the high-resolution remote sensing image road extraction algorithm based on texture characteristics assisted by other characteristic information is put forward. The specific process of road extraction in the algorithm is introduced, and the function of road extraction of urban high-resolution remote sensing image based on texture characteristics is also tested practically, the result shows that this method has a higher degree of accuracy in extracting roads from urban high-resolution remote sensing images.

scholarly journals Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2985 ◽  
Author(s):  
Yeneng Lin ◽  
Dongyun Xu ◽  
Nan Wang ◽  
Zhou Shi ◽  
Qiuxiao Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Network Models ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Proposed Model ◽  
Wide Range ◽  
Network Modules ◽  
Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

Road extraction from high-resolution remote sensing images based on characteristics

2007 ◽  
Author(s):  
Jie Yu ◽  
Huiling Qin ◽  
Qin Yan ◽  
Ming Tan ◽  
Guoning Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Road Extraction ◽  
Remote Sensing Images

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.

scholarly journals Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Linyi Li ◽  
Tingbao Xu ◽  
Yun Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Fuzzy Classification ◽  
Accuracy Evaluation ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Quantitative Accuracy ◽  
Extraction Algorithm

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

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