This page has only limited features, please log in for full access.

Unclaimed
Quanhua Zhao
Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Research article
Published: 18 January 2021 in International Journal of Remote Sensing
Reads 0
Downloads 0

Water monitoring is an important part of water resource protection. The extraction of water body from multispectral remote-sensing images has been proven to be an efficient and fast way for water monitoring. This paper presents a water body extraction algorithm from multispectral remote-sensing image based on region similarity and boundary information by combining adaptive band selection and over-segmentation. First of all, three bands are adaptively chosen by similarity-based band selection algorithm. Then, the image domain is partitioned into a series of homogeneous sub-regions by over-segmentation incorporating spectral and spatial information. On the sub-regions, the regional similarity is defined with respect to the similarities of texture and spectral features which are extracted using structure analysis method. After that, boundary information is extraction by Canny algorithm, then the water body is extracted by using the Fractal Net Evolution Approach (FNEA) which combines regional similarity and boundary information. The proposed algorithm is used to extract six water bodies with different complex texture backgrounds from multispectral sensors. According to the accuracy evaluation of water body extraction results, the overall accuracy (OA) is higher than 97.9100% and all Kappa coefficients (K) are up to 0.9436. We calculated the relative error (RE) of the area between the reference water body and the water body extracted by the proposed algorithm, the minimum and maximum relative error range is between [0.6180%, 7.7050%]. The experiments show that the proposed algorithm is feasible and effective.

ACS Style

Lingxiao Gu; Quanhua Zhao; Guanghui Wang; Yu Li. Water body extraction based on region similarity combined adaptively band selection. International Journal of Remote Sensing 2021, 42, 2963 -2980.

AMA Style

Lingxiao Gu, Quanhua Zhao, Guanghui Wang, Yu Li. Water body extraction based on region similarity combined adaptively band selection. International Journal of Remote Sensing. 2021; 42 (8):2963-2980.

Chicago/Turabian Style

Lingxiao Gu; Quanhua Zhao; Guanghui Wang; Yu Li. 2021. "Water body extraction based on region similarity combined adaptively band selection." International Journal of Remote Sensing 42, no. 8: 2963-2980.

Journal article
Published: 08 December 2020 in Remote Sensing
Reads 0
Downloads 0

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: 09 April 2020 in Remote Sensing
Reads 0
Downloads 0

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: 25 February 2020 in Remote Sensing
Reads 0
Downloads 0

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: 12 May 2017 in Sensors
Reads 0
Downloads 0

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: 01 January 2017 in Pattern Recognition Letters
Reads 0
Downloads 0

Using VT and HMRF model to improve robustness and noise insensitiveness. Combing VT-HMRF model into the FCM based framework. Comparing results with traditional FCM based algorithms. In this paper, we present new results related to the Voronoi Tessellation (VT) and Hidden Markov Random Field (HMRF) based Fuzzy C-Means (FCM) algorithm (VTHMRF-FCM) for texture image segmentation. In the VTHMRF-FCM algorithm, a VTHMRF model is established by using VT to partition an image domain into sub-regions (Voronoi polygons) and HMRF to describe the relationship of neighbor sub-regions. Based on the VTHMRF model, the objective function of VTHMRF-FCM is defined by adding a regularization term of Kullback-Leibler (KL) divergence information to FCM objective function. The proposed algorithm combines the benefits stemming from robust regional HMRF and FCM based clustering segmentation. Segmentation experiments on synthetic and real images by the proposed and other improved FCM algorithms are performed. Their results demonstrate that the proposed algorithm can obtain much better segmentation results than other FCM based methods.

ACS Style

Quan-Hua Zhao; Xiao-Li Li; Yu Li; Xue-Mei Zhao. A fuzzy clustering image segmentation algorithm based on Hidden Markov Random Field models and Voronoi Tessellation. Pattern Recognition Letters 2017, 85, 49 -55.

AMA Style

Quan-Hua Zhao, Xiao-Li Li, Yu Li, Xue-Mei Zhao. A fuzzy clustering image segmentation algorithm based on Hidden Markov Random Field models and Voronoi Tessellation. Pattern Recognition Letters. 2017; 85 ():49-55.

Chicago/Turabian Style

Quan-Hua Zhao; Xiao-Li Li; Yu Li; Xue-Mei Zhao. 2017. "A fuzzy clustering image segmentation algorithm based on Hidden Markov Random Field models and Voronoi Tessellation." Pattern Recognition Letters 85, no. : 49-55.

Research article
Published: 22 April 2016 in IET Computer Vision
Reads 0
Downloads 0

This study presents a region-based algorithm for segmenting colour texture image, which uses Voronoi tessellation for partitioning the domain of the image and Markov random field (MRF) for modelling colour texture. In detail, (i) an image domain is divided into polygons (or sub-regions) by Voronoi tessellation; (ii) two MRF models, improved Potts model and multivariate Gaussian MRF model, are used to characterise colour texture structures inter- and intra-polygons, respectively; (iii) by Bayesian paradigm, a posterior distribution which characterises the segmentation and model parameters conditional on a given colour image can be obtained up to a normalising constant; (iv) a Markov chain Monte Carlo algorithm is developed to simulate from the posterior distribution; finally, (v) a maximum a posteriori scheme is employed to find an optimal segmentation and model parameters. In order to evaluate the proposed colour texture segmentation algorithm, two kinds of colour texture images are tested, including synthetic and real colour texture images. The accuracy assessments are performed qualitatively on all kinds of images and quantitatively on synthetic images. All results demonstrate that the proposed algorithm is efficiently.

ACS Style

Quanhua Zhao; Yu Wang; Yu Li. Voronoi tessellation‐based regionalised segmentation for colour texture image. IET Computer Vision 2016, 10, 613 -622.

AMA Style

Quanhua Zhao, Yu Wang, Yu Li. Voronoi tessellation‐based regionalised segmentation for colour texture image. IET Computer Vision. 2016; 10 (7):613-622.

Chicago/Turabian Style

Quanhua Zhao; Yu Wang; Yu Li. 2016. "Voronoi tessellation‐based regionalised segmentation for colour texture image." IET Computer Vision 10, no. 7: 613-622.

Articles
Published: 04 March 2015 in International Journal of Remote Sensing
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.