Line-based image segmentation method: a new approach to segment VHSR remote sensing images automatically

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

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

Remote Sensing ◽

10.3390/rs13101903 ◽

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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Efficient Semantic Segmentation Method with Strip Pooling for VHR Remote Sensing Images

10.1109/igarss47720.2021.9553336 ◽

2021 ◽

Author(s):

Yifan Sheng ◽

Junli Yang ◽

Youguang Lin ◽

Yu Lei

Keyword(s):

Remote Sensing ◽

Semantic Segmentation ◽

Segmentation Method ◽

Remote Sensing Images

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Texture Image Segmentation Based on MRMRF in Contourlet Domain

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.732 ◽

2012 ◽

Vol 532-533 ◽

pp. 732-737

Author(s):

Xi Jie Wang ◽

Xiao Fan Zhao

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Field Model ◽

Model Parameters ◽

Texture Image ◽

Remote Sensing Images ◽

Unsupervised Segmentation ◽

Markov Random Field Model ◽

Markov Random ◽

Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

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Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.90-93.2836 ◽

2011 ◽

Vol 90-93 ◽

pp. 2836-2839 ◽

Cited By ~ 2

Author(s):

Jian Cui ◽

Dong Ling Ma ◽

Ming Yang Yu ◽

Ying Zhou

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Large Scale ◽

Mean Shift ◽

Image Features ◽

Small Scale ◽

Region Merging ◽

Segmentation Method ◽

Shift Method ◽

Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

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A Cloud-Contamination Removal Algorithm by Combining Image Segmentation and Level-set-based Approaches for Remote Sensing Images

2018 International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES) ◽

10.1109/icesit-icictes.2018.8442068 ◽

2018 ◽

Cited By ~ 2

Author(s):

Duangkamol Tedlek ◽

Sorasak Khoomboon ◽

Teerasit Kasetkasem ◽

Thitiporn Chanwimaluang ◽

Itsuo Kumazawa

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Level Set ◽

Remote Sensing Images ◽

Contamination Removal

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HIGH-RESOLUTION REMOTE SENSING IMAGE SEGMENTATION METHOD BASED ON SReLU

International Journal of Robotics and Automation ◽

10.2316/j.2019.206-0134 ◽

2019 ◽

Vol 34 (3) ◽

Author(s):

Chenming Li ◽

Xiaoyu Qu ◽

Yao Yang ◽

Hongmin Gao ◽

Yongchang Wang ◽

...

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

High Resolution ◽

Remote Sensing Image ◽

Segmentation Method

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Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

Remote Sensing ◽

10.3390/rs12244115 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4115

Author(s):

Xiaoli Li ◽

Jinsong Chen ◽

Longlong Zhao ◽

Shanxin Guo ◽

Luyi Sun ◽

...

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Noise Immunity ◽

Voronoi Tessellation ◽

Spectral Characteristics ◽

Remote Sensing Image ◽

Effective Characteristics ◽

Spatial Distance ◽

Remote Sensing Images ◽

Distance Weighted

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

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