A self-adaptive mean-shift segmentation approach based on graph theory for high-resolution remote sensing images

2015 ◽  
Author(s):  
Luwan Chen ◽  
Ling Han ◽  
Xiaohong Ning
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
High Resolution ◽  
Mean Shift ◽  
Remote Sensing Images ◽  
Segmentation Approach ◽  
Self Adaptive

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898 ◽  
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wenxing Bao ◽  
Xiuhong Yao
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
High Resolution ◽  
Texture Features ◽  
Original Image ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Spectrum Shape ◽  
Segmentation Accuracy ◽  
Matching Degree

According to the characteristics of high-resolution remote sensing (RS) images, a new multifeature segmentation method of high-resolution remote sensing images combining the spectrum, shape, and texture features based on graph theory is presented in the paper. Firstly, the quadtree segmentation method is used to partition the original image. Secondly, the spectrum, shape, and texture weight components are calculated all based on the constructed graph. The matching degree between pixels and the texture is computed similarity. Finally, the ratio cut standards combination of the spectrum, shape, and texture weight components is used for the final segmentation. The experimental results show that this method can obtain more ideal results and higher segmentation accuracy applied to RS image than those traditional methods.

scholarly journals A ROAD EXTRACTION METHOD BASED ON HIGH RESOLUTION REMOTE SENSING IMAGE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 671-676 ◽  
Author(s):  
X. Zhang ◽  
C. K. Zhang ◽  
H. M. Li ◽  
Z. Luo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Extraction Method ◽  
Mean Shift ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Mean Shift Algorithm ◽  
The Road ◽  
Stroke Width ◽  
Transformation Algorithm

Abstract. Aiming at the road extraction in high-resolution remote sensing images, the stroke width transformation algorithm is greatly affected by surrounding objects, and it is impossible to directly obtain high-precision road information. A new road extraction method combining stroke width transformation and mean drift is proposed. In order to reduce road holes and discontinuities, and preserve better edge information, the algorithm first performs denoising preprocessing by means of median filtering to the pre-processed image. Then, the mean shift algorithm is used for image segmentation. The adjacent parts of the image with similar texture and spectrum are treated as the same class, and then the fine areas less than the maximum stroke width are reduced. On the basis , the road information is extracted by the stroke width transformation algorithm, and the information also contains a small amount of interference information such as spots (non-road). In order to further improve road extraction accuracy and reduce speckle and non-road area interference, the basic operations and combinations in mathematical morphology are used to optimize it. The experimental results show that the proposed algorithm can accurately extract the roads on high-resolution remote sensing images, and the better the road features, the better the extraction effect. However, the applicability of the algorithm is greatly affected by the surrounding objects.

High-Resolution Remote Multi-Spectral Sensing Images Based on Texture Features

2014 ◽  
Vol 687-691 ◽  
pp. 3596-3599 ◽  
Author(s):  
Chen Ming Li ◽  
Li Qin Zhu ◽  
Qiang Wang ◽  
Zhen Sun ◽  
Feng Chen Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Texture Features ◽  
Remote Sensing Images ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Multispectral Sensing ◽  
Different Characteristics ◽  
Spot 5 ◽  
Spectral Sensing

Aiming at the difficulties in the segmentation for high-resolution remote multispectral sensing images, this paper proposed a segmentation approach for remote sensing images based on texture features. The algorithm implemented precipitation watershed transform respectively on the texture images obtained by the different characteristics of GLCM, and then superimposed the two segmentation results, finally completing the image segmentation by using a novel regional consolidation method that combined the texture features. The experiments were implemented on the high-resolution ALOS and SPOT 5 remote sensing images respectively. Compared with the traditional watershed segmentation approach based on gradient information, the experimental results showed that the proposed algorithm can accurately locate the edges of objects, effectively overcome the phenomenon of over-segmentation and under-segmentation, with a higher segmentation accuracy and stability.

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

2021 ◽  
Vol 13 (10) ◽  
pp. 1903
Author(s):  
Zhihui Li ◽  
Jiaxin Liu ◽  
Yang Yang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Stereo Matching ◽  
Mean Shift ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Edge Matching ◽  
Refinement Method ◽  
Initial Segmentation ◽  
Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

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