Search

Your search keyword '"Li, Xiaorun"' showing total 193 results
Start Over Author "Li, Xiaorun"
193 results on '"Li, Xiaorun"'

164. A New Scheme for Decomposition of Mixed Pixels Based on Modified Nonnegative Matrix Factorization and Genetic-Algorithm

Author

Zhang Kai, Zhao Liao-ying, Lv Yali, and Li Xiaorun

Subjects
education.field_of_study, Pixel, business.industry, Iterative method, Population, Hyperspectral imaging, Pattern recognition, Matrix decomposition, Non-negative matrix factorization, Genetic algorithm, Algorithm design, Artificial intelligence, business, education, Mathematics
Abstract

In the decomposition of mixed pixels of hyperspectral remote sensing images, the nonnegative matrix factorization (NMF) easily results in the problem of local minimum, owing to the influence of algorithm initializations. To solve the problem, this paper presents a new scheme based on the modified NMF (MNMF) algorithm and genetic algorithm (GA) to achieve the decomposition of mixed pixels. The endmembers obtained by MNMF is adopted as the initial individual population values of GA, the optimal solution of GA is in reverse as the new initial endmembers in the next running of MNMF, repeat this procedure until the global optimal solution is achieved. Experiment results based on simulated data and real hyperspectral imagery demonstrate that the proposed scheme outperforms NMF and MNMF.

Published
2009

175. Modified Linear-Prediction Based Band Selection for Hyperspectral Image

Author

厉小润 Li Xiaorun, 赵辽英 Zhao Liaoying, and 周杨 Zhou Yang

Subjects
Computer science, business.industry, Band selection, Hyperspectral imaging, Linear prediction, Pattern recognition, Artificial intelligence, business, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Image (mathematics)
Published
2013

179. Multi-spectral and panchromatic image fusion based on HIS-wavelet transform and MOPSO algorithm

Author

Zhao Liao-ying, Li Xiaorun, and Ma Qi-Liang

Subjects
Image fusion, Transformation (function), Computer science, Computer Science::Computer Vision and Pattern Recognition, Multispectral image, General Physics and Astronomy, Particle swarm optimization, Fusion rules, Wavelet transform, Noise (video), Algorithm, Panchromatic film
Abstract

Effective fusion method of remote sensing multispectral and panchromatic image must ensure maximizations of spectrum and space information. Using the fusion algorithm framework with combining HIS transformation and wavelet transform (HIS-wavelet), in this paper we propose a new method to extract high frequency coefficients and a new multispectral and panchromatic image fusion method by using multi-objective particle swarm optimization (MOPSO) algorithm. According to the physical characteristics that the edge information in the high frequency has the nature of direction and noise points in the high frequency are generally isolated, a high frequency coefficient extraction method based on Gauss first order differential is proposed. The final resulting image is optimally combined by two images obtained by using different fusion rules in HIS-wavelet. Multiple fusion evaluation indicators are used as object functions and the MOPSO algorithm is used to find the optimal weights. The experiments on TM multi-spectral image and SPOT panchromatic image are carried out. Experimental results demonstrate that the improved method has a better improvement in spectral and spatial information. At the same time, the resulting image which is obtained using MOPSO algorithm has obvious advantages in retaining the spectral information and the spatial information is also greatly improved.

Published
2012

181. Composite Kernel Target Detection Based on Mathematical Morphology for Hyperspectral Imagery

Author

崔建涛 Cui Jiantao, 沈银河 Shen Yinhe, 赵辽英 Zhao Liaoying, and 厉小润 Li Xiaorun

Subjects
Computer science, business.industry, Hyperspectral imaging, Computer vision, Pattern recognition, Artificial intelligence, Mathematical morphology, business, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Composite kernel
Published
2011

184. Nonlinear Spectral Mixture Analysis by Determining Per-Pixel Endmember Sets.

Author

Cui, Jiantao, Li, Xiaorun, and Zhao, Liaoying

Abstract

Nonlinear spectral mixture analysis is important when the light suffers multiple interactions among distinct materials. Few attempts have been conducted to incorporate spatial information to improve the performance of nonlinear unmixing algorithms. In this letter, local windows are adopted in the preliminary classification map to search the relevant endmembers for each pixel. Virtual endmembers, resulting from the relevant endmembers, represent the multiple-scattering effects in each pixel, and the corresponding abundances are estimated based on a modified bilinear model. Experiments on simulated and real hyperspectral images demonstrate that the proposed method provides a competitive or even better performance over some existing algorithms. [ABSTRACT FROM PUBLISHER]

Published
2014
Full Text
View/download PDF

186. Spectral Analysis for Subpixel Materials Based on Kernel Partial Nonnegative Matrix Factorization.

Author

CUI Jiantao, LI Xiaorun, and ZHAO Liaoying

Abstract

To improve the accuracy of spectral analysis for subpixel materials further, a nonlinear unmixing algorithm based on the kernel partial nonnegative matrix factorization (KPNMF) was proposed. Firstly, an endmember extraction algorithm based on the theory of convex geometry was used to generate a candidate pixel set of pure endmembers, and then the pure endmember was determined according to the spatial purity indices of the candidate pixels. Given information of pure endmembers, the kernel method was adopted to extend partial nonnegative matrix factorization (PNMF). The corresponding objective function was constructed, and the iterative solution was also derived to obtain the subpixel endmembers and abundances of all the endmembers. The experimental results demonstrate that the proposed unmixing algorithm has good nonlinear unmixing ability, and the unmixing results are better than those of linear unmixing algorithms. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

190. Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement.

Author

Zhan, Shuyue, Wang, Chao, Liu, Shuchang, Xia, Kaibo, Huang, Hui, Li, Xiaorun, Liu, Caicai, and Xu, Ren

Subjects
ALGORITHMS, MARKET segmentation, IMAGE processing, XYLENE, OIL spills
Abstract

Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algorithm for the target enhancement of xylene spills and their segmentation in ultraviolet (UV) images. To improve the contrast between targets and backgrounds (waves, sun reflections, and shadows), we developed a global background suppression (GBS) method to remove the irrelevant objects from the background, which is followed by an adaptive target enhancement (ATE) method to enhance the target. Based on the histogram information of the processed image, we designed an automatic algorithm to calculate the optimal number of clusters, which is usually manually determined in traditional cluster segmentation methods. In addition, necessary pre-segmentation processing and post-segmentation processing were adopted in order to improve the performance. Experimental results on our UV image datasets demonstrated that the proposed method can achieve good segmentation results for chemical spills from different backgrounds, especially for images with strong waves, uneven intensities, and low contrast. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results