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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 4
1221-1225,1238

Unmixing of hyperspectral images based on endmember estimation of artificial neural network

Zhang Heng1
Jia Zhicheng1
Chen Lei2,3
Guo Yanju1
1. School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, China
2. School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
3. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0777

Abstract

Aimed at the problems of hyperspectral images unmixing, it is found that the unmixing accuracy of the traditional unmixing algorithm is not high when the number of endmember keeps constant in unmixing. Thus, based on the ANN, this paper proposed a novel unmixing algorithm of estimating the number and category of endmember in a single pixel. Firstly, the unmixing algorithm used the ANN to estimate the number and category of each mixed pixel's endmember in the remote sensing image. Then, it determined the objective function of the algorithm based on the estimation results, and introduced the improved differential search algorithm to solve the objective function. Finally, it obtained the abundances and the parameters to realize the unmixing of hyperspectral images. The experimental results on simulated and real hyperspectral data demonstrate that compared with the existing unmixing algorithms, the proposed unmixing algorithm has higher performance and is more in line with the actual scene.

Foundation Support

国家自然科学基金资助项目(61401307)
天津市应用基础与前沿技术研究计划资助项目(15JCYBJC17100)
中国博士后科学基金资助项目(2014M561184)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0777
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Technology of Graphic & Image
Pages: 1221-1225,1238
Serial Number: 1001-3695(2020)04-056-1221-05

Publish History

[2020-04-05] Printed Article

Cite This Article

张衡, 贾志成, 陈雷, 等. 基于ANN端元估计的高光谱图像解混算法 [J]. 计算机应用研究, 2020, 37 (4): 1221-1225,1238. (Zhang Heng, Jia Zhicheng, Chen Lei, et al. Unmixing of hyperspectral images based on endmember estimation of artificial neural network [J]. Application Research of Computers, 2020, 37 (4): 1221-1225,1238. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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