Road Extraction Based on the Algorithms of K-Means Clustering and Hybrid Model of SVM and FCM

2012 ◽  
Vol 518-523 ◽  
pp. 5738-5743 ◽  
Author(s):  
Da Ming Zhu ◽  
Xiang Wen ◽  
Rong Xia
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Information Extraction ◽  
Hybrid Model ◽  
Remote Sensing Image ◽  
Road Extraction ◽  
Supervised Clustering ◽  
Fuzzy C Means ◽  
The Road

Information extraction is the prerequisite of remote sensing image segmentation, which is the key procedure of image analysis. In this paper hard C-means and fuzzy C-means is adopted for segmentation in remote sensing image to realize our road extraction. Firstly, we proposed k-means for image segmentation using non-supervised clustering, and we can achieve our aim finally. Meanwhile, SVM combined with Fuzzy C means was proposed and this model was implemented in remote sensing image segmentation to extract the road net. Finally the comparison with two proposed algorithm was carried out, and after experiment, SVM plus FCM model is much more accurate than k-means.

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 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.

scholarly journals Remote Sensing Road Extraction by Road Segmentation Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5050
Author(s):  
Jiahai Tan ◽  
Ming Gao ◽  
Kai Yang ◽  
Tao Duan
Keyword(s):  
Remote Sensing ◽  
Attention Mechanism ◽  
Context Information ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Long Distance ◽  
The Road ◽  
Road Segmentation ◽  
Context Characteristics

Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.

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