A research of road centerline extraction algorithm from high resolution remote sensing images

2017 ◽  
Author(s):  
Tingfa Xu ◽  
Yushan Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Images ◽  
Centerline Extraction ◽  
Extraction Algorithm

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.

scholarly journals MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

2021 ◽  
Vol 13 (2) ◽  
pp. 239
Author(s):  
Zhenfeng Shao ◽  
Zifan Zhou ◽  
Xiao Huang ◽  
Ya Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Google Earth ◽  
Road Surface ◽  
Remote Sensing Images ◽  
Surface Extraction ◽  
The Road ◽  
Centerline Extraction ◽  
Very High

Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction. Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth. Therefore, the studies on road surface extraction and road centerline extraction under complex scenes are insufficient. Meanwhile, most existing efforts addressed these two tasks separately, without considering the possible joint extraction of road surface and centerline. With the introduction of multitask convolutional neural network models, it is possible to carry out these two tasks simultaneously by facilitating information sharing within a multitask deep learning model. In this study, we first design a challenging dataset using remote sensing images from the GF-2 satellite. The dataset contains complex road scenes with manually annotated images. We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction. We take features extracted from the road as the condition of centerline extraction, and the information transmission and parameter sharing between the two tasks compensate for the potential problem of insufficient road centerline samples. In the network design, we use atrous convolutions and a pyramid scene parsing pooling module (PSP pooling), aiming to expand the network receptive field, integrate multilevel features, and obtain more abundant information. In addition, we use a weighted binary cross-entropy function to alleviate the background imbalance problem. Experimental results show that the proposed algorithm outperforms several comparative methods in the aspects of classification precision and visual interpretation.

scholarly journals Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenbing Yang ◽  
Xiaoqi Gao ◽  
Chunlei Zhang ◽  
Feng Tong ◽  
Guantian Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Image ◽  
Structural Similarity ◽  
Remote Sensing Images ◽  
The Road ◽  
Extraction Algorithm ◽  
Length Width ◽  
On The Road

This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets.

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.

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