scholarly journals RESEARCH ON BROKEN ROAD CONNECTION METHOD AFTER ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 387-395
Author(s):  
D. L. Fan ◽  
B. Wang ◽  
Z. L. Chen ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Power Grids ◽  
Road Extraction ◽  
The Road ◽  
Isolated Points

Abstract. Aiming at the problem of disconnection after road classification of remote sensing image, this paper proposes an optimization method for broken road connection considering spatial connectivity. The method extracts the road skeleton based on the binarized image after road extraction, and uses the eight neighborhood detection algorithm to find the road breakpoints after road extraction of high-resolution remote sensing image, and removes the isolated points of the road edge according to mathematical morphology filtering. Secondly, use K-means clustering algorithm to search for road breakpoints, and eliminate invalid breakpoints; then, fit the breakpoints of each category through polynomial curves, and record the mathematics of each fitted curve expression; Finally, the coordinate sequences between each kind of breakpoint is calculated according to each fitted polynomial, and the corresponding pixel is filled with the width of the road to realize automatic detection and connection. In this paper, the images after road extraction based on the U-Net network is used to test the method. The results show that the proposed method can better connect the roads formed by road or building shadows. Especially, the single broken road , has a high integrity of the road shape after repairing. The method proposed in this paper has certain reference significance for the classification and repair of linear objects such as roads, power grids and tracks.

A Fast Algorithm for Road Recognition in Remote Sensing Image

2010 ◽  
Vol 108-111 ◽  
pp. 1344-1347
Author(s):  
Li Li Li ◽  
Yong Xin Liu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fast Algorithm ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Feature Integration ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Simulation Results

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

scholarly journals A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 385
Author(s):  
Rui Xu ◽  
Yanfang Zeng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Kernel Learning ◽  
Morphological Operations ◽  
Road Extraction ◽  
The Road ◽  
Topological Features ◽  
Important Research Topic ◽  
Wide Range ◽  
Quantitative Results

Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel method for road extraction from HRRS images. Multi-kernel learning is first utilized to integrate the spectral, texture, and linear features of images to classify the images into road and non-road groups. A precise extraction method for road elements is then designed by building road shaped indexes to automatically filter out the interference of non-road noises. A series of morphological operations are also carried out to smooth and repair the structure and shape of the road element. Finally, based on the prior knowledge and topological features of the road, a set of penalty factors and a penalty function are constructed to connect road elements to form a complete road network. Experiments are carried out with different sensors, different resolutions, and different scenes to verify the theoretical analysis. Quantitative results prove that the proposed method can optimize the weights of different features, eliminate non-road noises, effectively group road elements, and greatly improve the accuracy of road recognition.

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

Rural road extraction from high resolution remote sensing image

2015 ◽  
pp. 77-84
Author(s):  
Zh. Sun ◽  
Q. Meng ◽  
Z. Miao ◽  
X. Gu ◽  
Y. Zhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Road Extraction ◽  
Rural Road
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