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Yu Li
School of Geomatics, Liaoning Technical University, Fuxin 123000, China

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Journal article
Published: 08 December 2020 in Remote Sensing
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An urban riverway extraction method is proposed for high-resolution synthetic aperture radar (SAR) images. First, the original image is partitioned into overlapping sub-image blocks, in which the sub-image blocks that do not cover riverways are regarded as background. Sub-image blocks covering riverways are then filtered using the iterative adaptive speckle reduction anisotropic diffusion (SRAD) that introduces the relative signal-to-noise ratio (RSNR). The filtered images are segmented quickly by the Sauvola algorithm, and the false riverway fragments are removed by the area and aspect ratio of the connected component in the segmentation results. Using the minimum convex hull of each riverway segment as the connection object, the seeds are automatically determined by the difference between adjacent pyramid layers, and the sub-image block riverway extraction result is used as the bottom layer. The discontinuity connection between river segments is achieved by multi-layer region growth. Finally, the processed sub-image blocks are stitched to get the riverway extraction results for the entire image. To verify the applicability and usefulness of the proposed approach, high-resolution SAR imagery obtained by the Gaofen-3 (GF-3) satellite was used in the assessment. The qualitative and quantitative evaluations of the experimental results show that the proposed method can effectively and completely extract complex urban riverways from high-resolution SAR images.

ACS Style

Yu Li; Yun Yang; Quanhua Zhao. Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection. Remote Sensing 2020, 12, 4014 .

AMA Style

Yu Li, Yun Yang, Quanhua Zhao. Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection. Remote Sensing. 2020; 12 (24):4014.

Chicago/Turabian Style

Yu Li; Yun Yang; Quanhua Zhao. 2020. "Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection." Remote Sensing 12, no. 24: 4014.

Journal article
Published: 03 July 2020 in Signal Processing
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In general, the existing spacial filters occupy themselves in smoothing out the speckle noises pixel by pixel in a local or non-local way, without preserving the information hiding in the speckle noises. This paper presents a new filtering paradigm that aims to restore the statistical characteristic of speckle distribution, rather than simply smoothing them. Firstly, under the assumption that the statistical distribution function corresponding to the homogeneous regions in a given noising SAR image is known a priori, and their distribution parameters can be estimated with the values of pixels in these regions. Several gray levels with large deviation are selected by comparing the histogram and the statistical distribution function. Then, the pixels taking these gray levels as their values can be spatially located in the image domain, some of which are considered as abnormal pixels after abnormal pixels detection based on ratio pixel relativity measure, and these abnormal pixels are to be filtered. Finally, repeat the above procedure until the histogram of filtered image fits the distribution function well. Tests on real SAR images show that the proposed method can obtain better statistical modeling results, and simultaneously achieve noise suppression to some extent while maintaining image quality better.

ACS Style

Yu Li; Shuyun Wang; Quanhua Zhao; Guanghui Wang. A new SAR image filter for preserving speckle statistical distribution. Signal Processing 2020, 176, 107706 .

AMA Style

Yu Li, Shuyun Wang, Quanhua Zhao, Guanghui Wang. A new SAR image filter for preserving speckle statistical distribution. Signal Processing. 2020; 176 ():107706.

Chicago/Turabian Style

Yu Li; Shuyun Wang; Quanhua Zhao; Guanghui Wang. 2020. "A new SAR image filter for preserving speckle statistical distribution." Signal Processing 176, no. : 107706.

Journal article
Published: 09 April 2020 in Remote Sensing
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The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.

ACS Style

Xue Shi; Yu Li; Quanhua Zhao. Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation. Remote Sensing 2020, 12, 1219 .

AMA Style

Xue Shi, Yu Li, Quanhua Zhao. Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation. Remote Sensing. 2020; 12 (7):1219.

Chicago/Turabian Style

Xue Shi; Yu Li; Quanhua Zhao. 2020. "Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation." Remote Sensing 12, no. 7: 1219.

Journal article
Published: 01 March 2020 in Remote Sensing
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With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.

ACS Style

Wenjie Lin; Yu Li. Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree. Remote Sensing 2020, 12, 783 .

AMA Style

Wenjie Lin, Yu Li. Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree. Remote Sensing. 2020; 12 (5):783.

Chicago/Turabian Style

Wenjie Lin; Yu Li. 2020. "Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree." Remote Sensing 12, no. 5: 783.

Journal article
Published: 25 February 2020 in Remote Sensing
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This paper presents a regionalized segmentation method for synthetic aperture radar (SAR) intensity images based on tessellation with irregular polygons. In the proposed method, the image domain is partitioned into a collection of irregular polygons, which are constructed using sets of nodes and are used to fit homogeneous regions with arbitrary shapes. Each partitioned polygon is taken as the basic processing unit. Assuming the intensities of the pixels in the polygon follow an independent and identical gamma distribution, the likelihood of the image intensities is modeled. After defining the prior distributions of the tessellation and the parameters for the likelihood model, a posterior probability model can be built based on the Bayes theorem as a segmentation model. To obtain optimal segmentation, a reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model, where the move operations include updating the gamma distribution parameter, updating labels, moving nodes, merging polygons, splitting polygons, adding nodes, and deleting nodes. Experiments were carried out on synthetic and real SAR intensity images using the proposed method while the regular and Voronoi tessellation-based methods were also preformed for comparison. Our results show the proposed method overcomes some intrinsic limitations of current segmentation methods and is able to generate good results for homogeneous regions with different shapes.

ACS Style

Quanhua Zhao; Hongyun Zhang; Guanghui Wang; Yu Li. Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image. Remote Sensing 2020, 12, 753 .

AMA Style

Quanhua Zhao, Hongyun Zhang, Guanghui Wang, Yu Li. Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image. Remote Sensing. 2020; 12 (5):753.

Chicago/Turabian Style

Quanhua Zhao; Hongyun Zhang; Guanghui Wang; Yu Li. 2020. "Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image." Remote Sensing 12, no. 5: 753.

Journal article
Published: 25 November 2019 in Remote Sensing
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Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

ACS Style

Yan Xu; Ruizhi Chen; Yu Li; Peng Zhang; Jie Yang; Xuemei Zhao; Mengyun Liu; Dewen Wu. Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model. Remote Sensing 2019, 11, 2772 .

AMA Style

Yan Xu, Ruizhi Chen, Yu Li, Peng Zhang, Jie Yang, Xuemei Zhao, Mengyun Liu, Dewen Wu. Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model. Remote Sensing. 2019; 11 (23):2772.

Chicago/Turabian Style

Yan Xu; Ruizhi Chen; Yu Li; Peng Zhang; Jie Yang; Xuemei Zhao; Mengyun Liu; Dewen Wu. 2019. "Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model." Remote Sensing 11, no. 23: 2772.

Article
Published: 08 November 2017 in International Journal of Fuzzy Systems
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In complex color images, colors inside a homogeneous region might be contradistinctive and the distribution could not be described by a simple Gaussian distribution as used in traditional image segmentation algorithms. Based on the characteristics that the red, green, and blue color planes are not independent and pixels in the same neighborhood system might stand for the same object, we introduce a Gaussian model containing the interactions between different color planes to strengthen the connections both on a color plane and between color planes in a neighborhood system. Consequently, a Gaussian mixture model with the prior distribution, defined by Markov random field and acting as the weight, is employed to describe the distribution of color measures inside a homogeneous region. With the Gaussian mixture model containing the interactions between color planes, we proposed a fuzzy clustering approach for complex color image segmentation. Experiments on synthetic and real-color images, in which homogeneous regions are complex, show that the proposed algorithm compares favorably with the compared algorithms developed for the same purpose.

ACS Style

Xuemei Zhao; Yu Li; Quanhua Zhao. A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model. International Journal of Fuzzy Systems 2017, 20, 309 -317.

AMA Style

Xuemei Zhao, Yu Li, Quanhua Zhao. A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model. International Journal of Fuzzy Systems. 2017; 20 (1):309-317.

Chicago/Turabian Style

Xuemei Zhao; Yu Li; Quanhua Zhao. 2017. "A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model." International Journal of Fuzzy Systems 20, no. 1: 309-317.

Journal article
Published: 12 May 2017 in Sensors
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This paper presents a novel multilook SAR image segmentation algorithm with an unknown number of clusters. Firstly, the marginal probability distribution for a given SAR image is defined by a Gamma mixture model (GaMM), in which the number of components corresponds to the number of homogeneous regions needed to segment and the spatial relationship among neighboring pixels is characterized by a Markov Random Field (MRF) defined by the weighting coefficients of components in GaMM. During the algorithm iteration procedure, the number of clusters is gradually reduced by merging two components until they are equal to one. For each fixed number of clusters, the parameters of GaMM are estimated and the optimal segmentation result corresponding to the number is obtained by maximizing the marginal probability. Finally, the number of clusters with minimum global energy defined as the negative logarithm of marginal probability is indicated as the expected number of clusters with the homogeneous regions needed to be segmented, and the corresponding segmentation result is considered as the final optimal one. The experimental results from the proposed and comparing algorithms for simulated and real multilook SAR images show that the proposed algorithm can find the real number of clusters and obtain more accurate segmentation results simultaneously.

ACS Style

Quanhua Zhao; Xiaoli Li; Yu Li. Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering. Sensors 2017, 17, 1114 .

AMA Style

Quanhua Zhao, Xiaoli Li, Yu Li. Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering. Sensors. 2017; 17 (5):1114.

Chicago/Turabian Style

Quanhua Zhao; Xiaoli Li; Yu Li. 2017. "Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering." Sensors 17, no. 5: 1114.

Journal article
Published: 21 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Different classes, to estimate the number of classes in image segmentation issues. In this strategy, the information of a homogeneous region is measured by entropy. Then a region is considered to be disordered and should be split if its entropy is more than a given threshold. On the contrary, when the KL information of two homogeneous regions is less than a threshold, it is believed that they are similar and should be merged. The entropy-KL strategy can be combined with any kind of segmentation algorithm since it uses the information and distance as a general way to decide the number of classes. In this paper, the HMRF-FCM algorithm is employed as the segmentation process and combined with the entropy-KL strategy to induce a segmentation algorithm which can fix the number of classes automatically. The proposed algorithm is performed on synthetic image, real panchromatic images and SAR images to demonstrate the effectiveness.

ACS Style

Xuemei Zhao; Yu Li; Quanhua Zhao; Chunyan Wang. AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B7, 437 -441.

AMA Style

Xuemei Zhao, Yu Li, Quanhua Zhao, Chunyan Wang. AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B7 ():437-441.

Chicago/Turabian Style

Xuemei Zhao; Yu Li; Quanhua Zhao; Chunyan Wang. 2016. "AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7, no. : 437-441.

Articles
Published: 04 March 2015 in International Journal of Remote Sensing
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This article presents a statistics- and region-based approach to segmentation of synthetic aperture radar (SAR) images. The proposed approach can automatically determine the number of classes and segment the image simultaneously. First of all, an image domain is partitioned into a set of blocks by regular tessellation and the image is modelled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent gamma distribution. The Bayesian paradigm is followed to build an image segmentation model. Then, a Reversible Jump Markov Chain Monte Carlo scheme is designed to simulate the segmentation model, which determines the number of classes and segments the image roughly. Furthermore, in order to improve the accuracy of the segmentation results, refined operation is performed. The results obtained from both real and simulated SAR images show that the proposed approach works well and efficient.

ACS Style

Yu Wang; Yu Li; Quanhua Zhao. Segmentation of high-resolution SAR image with unknown number of classes based on regular tessellation and RJMCMC algorithm. International Journal of Remote Sensing 2015, 36, 1290 -1306.

AMA Style

Yu Wang, Yu Li, Quanhua Zhao. Segmentation of high-resolution SAR image with unknown number of classes based on regular tessellation and RJMCMC algorithm. International Journal of Remote Sensing. 2015; 36 (5):1290-1306.

Chicago/Turabian Style

Yu Wang; Yu Li; Quanhua Zhao. 2015. "Segmentation of high-resolution SAR image with unknown number of classes based on regular tessellation and RJMCMC algorithm." International Journal of Remote Sensing 36, no. 5: 1290-1306.

Journal article
Published: 25 October 2013 in Sensors
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This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage.

ACS Style

Quanhua Zhao; Yu Li; Zhenggang Liu. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction. Sensors 2013, 13, 14484 -14499.

AMA Style

Quanhua Zhao, Yu Li, Zhenggang Liu. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction. Sensors. 2013; 13 (11):14484-14499.

Chicago/Turabian Style

Quanhua Zhao; Yu Li; Zhenggang Liu. 2013. "SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction." Sensors 13, no. 11: 14484-14499.