Hyperspectral data fusion technology for weak small sea surface target

Zhu Yuanyuan; Mu Wei; Li Guangliang; Wang Hu; Liu Tong; Gao Zedong

doi:10.5768/JAO201738.0102001

Journal of Applied Optics > 2017 > 38(1): 37-41. > DOI: 10.5768/JAO201738.0102001

Zhu Yuanyuan, Mu Wei, Li Guangliang, Wang Hu, Liu Tong, Gao Zedong. Hyperspectral data fusion technology for weak small sea surface target[J]. Journal of Applied Optics, 2017, 38(1): 37-41. DOI: 10.5768/JAO201738.0102001

Citation:

PDF (1133 KB)

Hyperspectral data fusion technology for weak small sea surface target

Xi'an Institute of Applied Optics, Xi'an 710065, China

More Information

Received Date: August 22, 2016
Revised Date: October 22, 2016

Graphical Abstract

Abstract

Abstract

Aiming at solving problem of weak small target missing during dimension reduction and fusion of hyperspectral data, hyperspectral data fusion method for weak small target is studied in this paper. Class pixel matrix is produced by similarity measures, dimension reduction projection matrix is calculated by principal component transforming class pixel matrix, and dimension reduction data is obtained by dimension reduction projection matrix projecting the original spectral data. False-color result image is produced by RGB color space transforming from reduction data. Fusion performance of improved method is verified by hyperspectral data of weak small target and underwater fish group. Experiments show that similarity classification fusion method can not only get one false color image from high dimension hyperspectral data, but also avoid missing weak small targets from background, which enhances abilities of detection and identification, and has a strong application prospects.

FullText(HTML)

References (13)

References

[1]	苏晓丹.舰艇光电探测系统发展[J].飞航导弹, 2014(12):62-64. http://d.old.wanfangdata.com.cn/Periodical/fhdd201412014 Su Xiaodan.Ships photoelectric detection system development[J]. Winged Missiles Journal, 2014(12):62-64. http://d.old.wanfangdata.com.cn/Periodical/fhdd201412014
[2]	马优恒, 陆培国, 郭渝林, 等.舰载光电系统的环境适应性设计[J].应用光学, 2014, 35(3):371-376. http://d.old.wanfangdata.com.cn/Periodical/yygx201403004 Ma Youheng, Lu Peiguo, Guo Yulin, et al.Environmental worthiness design of shipborne electro-optical system[J]. Journal of Applied Optics, 2014, 35(3):371-376. http://d.old.wanfangdata.com.cn/Periodical/yygx201403004
[3]	Shaw G A, Burke H K. Spectral imaging for remote sensing[J]. Lincoln Laboratory Journal, 2003, 14(1): 3-28. http://d.old.wanfangdata.com.cn/OAPaper/oai_pubmedcentral.nih.gov_3841904
[4]	Manolakis D, Marden D, Shaw G A. Hyperspectral image processing for automatic target detection[J]. Lincoln Laboratory Journal, 2003, 14(1): 79-116. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_ec1eaca88a20f6136970335a3739acd5
[5]	余旭初, 冯伍法, 杨国鹏, 等.高光谱影像分析与应用[M].北京:科学出版社, 2013. Yu Xuchu, Feng Wufa, Yang Guopeng, et al.Hyperspectral image analysis and application[M]. Beijing:Science Press, 2013.
[6]	Chang C I. Hyperspectral data processing algorithm design and analysis[M]. New Jersey:John Wiley & Sons, 2013.
[7]	朱院院, 高教波, 高泽东, 等.基于相似性分类的高光谱主成分融合方法比较[J].光学技术, 2016, 42(2):97-102. http://d.old.wanfangdata.com.cn/Periodical/gxjs201602001 Zhu Yuanyuan, Gao Jiaobo, Gao Zedong, et al.Comparison of principal component fusion method of hyperspectral image based on similarity classification[J]. Optical Technique, 2016, 42(2):97-102. http://d.old.wanfangdata.com.cn/Periodical/gxjs201602001
[8]	朱院院, 高教波, 高泽东, 等.基于多区间映射评价优选的光谱数据融合显示算法[J].应用光学, 2015, 36(5):728-734. http://d.old.wanfangdata.com.cn/Periodical/yygx201505011 Zhu Yuanyuan, Gao Jiaobo, Gao Zedong, et al.Fusion and vision algorithm of spectral data based on mapping-evaluating-optimizing methods within multi-section[J]. Journal of Applied Optics, 2015, 36(5):728-734. http://d.old.wanfangdata.com.cn/Periodical/yygx201505011
[9]	Achalakul T, Taylor S. A concurrent spectral-screening PCT algorithm for remote sensing application[J]. Journal of Information Fusion, 2000(1/2): 89-97. http://cn.bing.com/academic/profile?id=2a7373d89fb9243fe5c0486660a14e57&encoded=0&v=paper_preview&mkt=zh-cn
[10]	张浚哲, 朱文泉, 郑周涛, 等.高光谱数据的相似性测度对比研究[J].测绘科学, 2013, 38(6):33-36. http://d.old.wanfangdata.com.cn/Periodical/chkx201306010 Zhang Junzhe, Zhu Wenquan, Zheng Zhoutao, et al. Comparative study on similarity measures of hyperspectral remote sensing data[J]. Science of Surveying and Mapping, 2013, 38(6):33-36. http://d.old.wanfangdata.com.cn/Periodical/chkx201306010
[11]	闻兵工, 冯伍法, 刘伟, 等.基于光谱曲线整体相似性测度的匹配分类[J].测绘科学技术学报, 2009, 26(2):128-131. doi: 10.3969/j.issn.1673-6338.2009.02.014 Wen Binggong, Feng Wufa, Liu Wei, et al.Matching and classification based on the whole comparability measure of spectral curve[J]. Journal of Geomatics Science and Technology, 2009, 26(2):128-131. doi: 10.3969/j.issn.1673-6338.2009.02.014
[12]	董广军, 张永生, 戴晨光, 等.基于信息散度特征的高光谱影像识别技术[J].仪器仪表学报, 2006, 27(6):2091-2092. http://d.old.wanfangdata.com.cn/Periodical/yqyb2006z3128 Dong Guangjun, Zhang Yongsheng, Dai Chenguang, et al. Target recognition for hyperspectral image based on SID spectral feature[J]. Chinese Journal of Scientific Instrument, 2006, 27(6):2091-2092. http://d.old.wanfangdata.com.cn/Periodical/yqyb2006z3128
[13]	Fairchild M D. Color appearance models[M]. 3nd ed. West Sussex: John Wiley & Sons, 2013.

[1]	CAI Wei, WANG Xin, JIANG Xinhao, YANG Zhiyong, CHEN Dong. Few shot target detection method based on background suppression and classification correction[J]. Journal of Applied Optics, 2024, 45(2): 405-414. DOI: 10.5768/JAO202445.0203002
[2]	BI Junbo, LI Guoping, LI Meng, LIU Haining. Yarn paper tube classification method with multi-linked fusion optimized template matching[J]. Journal of Applied Optics, 2024, 45(2): 365-372. DOI: 10.5768/JAO202445.0202003
[3]	HOU Jinyao, LIU Weiguo, ZHOU Shun, GAO Aihua, GE Shaobo, XIAO Xiangguo. Image classification of optical element surface defects based on convolutional neural network[J]. Journal of Applied Optics, 2023, 44(3): 677-683. DOI: 10.5768/JAO202344.0305003
[4]	LU Hongqiang, Wang Junlin, WANG Ya’nan, AN Xuezhi, NING Xinchao, QIAN kun. Fine-grained target classification method based on deep clustering[J]. Journal of Applied Optics, 2022, 43(4): 669-675. DOI: 10.5768/JAO202243.0402001
[5]	GU Jing, ZHANG Keshuai, ZHU Yiman. Research on weld defect image classification based on convolutional neural network[J]. Journal of Applied Optics, 2020, 41(3): 531-537. DOI: 10.5768/JAO202041.0302007
[6]	YOU Anqing, PAN Xudong, ZHAO Ping, PAN Wenwu. Research on automatic classification of point cloud scanned by lidar during power-line patrol[J]. Journal of Applied Optics, 2019, 40(6): 1077-1083. DOI: 10.5768/JAO201940.0602005
[7]	Zhao Song, Feng Xiang. Sparse coding based spatial pyramid matching algorithm for image classification[J]. Journal of Applied Optics, 2016, 37(5): 706-711. DOI: 10.5768/JAO201637.0502006
[8]	an Le-le, Li Hui, Qiu Ju-neng, Liang Ping. Image quality assessment method based on regional contrast and structural similarity[J]. Journal of Applied Optics, 2015, 36(1): 58-63. DOI: 10.5768/JAO201536.0102002
[9]	Yang Li-hong, Xie Hui, Su Jun-hong. Surface characteristics study of laser-induced thin film damagebased on similarity measure algorithm[J]. Journal of Applied Optics, 2014, 35(6): 1099-1103.
[10]	JIA Jun-tao. Infrared pedestrian detection method using series combination classification[J]. Journal of Applied Optics, 2012, 33(4): 770-773.

Cited By

Cited by

Periodical cited type(0)

Other cited types(1)

Get Citation

PDF

XML

Article views (623) PDF downloads (89) Cited by(1)

Turn off MathJax

Article Contents

Abstract

References

Hyperspectral data fusion technology for weak small sea surface target

Abstract

References

Related Articles

Cited by

Periodical cited type(0)

Other cited types(1)

Catalog

Related Databases

Advertising cooperation

Links

Help Center

Hyperspectral data fusion technology for weak small sea surface target

Abstract

References

Related Articles

Cited by

Periodical cited type(0)

Other cited types(1)

Catalog

Related Databases

Advertising cooperation

Links

Help Center

Export File

Citation

Format

Content