| 光谱学与光谱分析 |
|
|
|
|
|
| Research on Airborne Hyperspectral Identification of Red Tide Organism Dominant Species Based on SVM |
| MA Yi1, 2, ZHANG Jie1, 2,CUI Ting-wei1 |
1. First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
2. Key Laboratory of Marine Science and Numerical Modeling, State Oceanic Administration, Qingdao 266061, China |
|
|
|
|
Abstract Airborne hyperspectral identification of red tide organism dominant species can provide technique for distinguishing red tide and its toxin, and provide support for scaling the disaster. Based on support vector machine(SVM), the present paper provides an identification model of red tide dominant species. Utilizing this model, the authors accomplished three identification experiments with the hyperspectral data obtained on 16th July, and 19th and 25th August, 2001. It is shown from the identification results that the model has a high precision and is not restricted by high dimension of the hyperspectral data.
|
|
Received: 2005-08-26
Accepted: 2005-11-28
|
|
|
|
Corresponding Authors:
MA Yi
|
|
| Cite this article: |
|
MA Yi,ZHANG Jie,CUI Ting-wei. Research on Airborne Hyperspectral Identification of Red Tide Organism Dominant Species Based on SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(12): 2302-2305.
|
|
|
|
| URL: |
|
https://www.gpxygpfx.com/EN/Y2006/V26/I12/2302 |
[1] MA Yi, ZHANG Jie(马 毅, 张 杰). Advances in Marine Sciences(海洋科学进展), 2002, 20(4): 94.
[2] Laurie L Richardson, Fred A Kruse. NASA Aivbome Visible/Intraed Imaging Spectrometer Proceedings, 1999.
[3] Laurie L. Richardson, Fred A. Kruse. NASA Aivbome Visible/Intraed Imaging Spectrometer Proceedings, 2000.
[4] CUI Ting-wei, ZHANG Jie, MA Yi, et al(崔廷伟, 张 杰, 马 毅, 等). Oceanologia et Limnologia Sinica(海洋与湖沼), 2005, 36(3): 277.
[5] Ma Yi, Zhang Jie. SPIE' 2002, Hangzhou, China, 2003, 4892: 278.
[6] Vapnik V N. The Nature of Statistical Learning Theory. NY: Springer-Verlag, 1995.
[7] QIN Dong-mei, HU Zhan-yi, ZHAO Yong-heng(覃冬梅, 胡占义, 赵永恒). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(4): 507.
[8] ZHANG Lu-da, SU Shi-guang, WANG Lai-sheng, et al(张录达, 苏时光, 王来生, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(1): 33.
[9] A Tutorial on Support Vector Machines for Pattern Recognition. Http://svm.research.bell-labs.com
[10] ZHAO Dong-zhi(赵冬至). Research Corpus of Redtide Monitoring and Evaluation in Bohai(渤海赤潮灾害监测与评估研究文集). Beijing: Ocean Press(北京: 海洋出版社), 2000. 60, 117.
[11] Cui Tingwei, Zhang Jie, Zhang Hongliang, et al. SPIE’2002, Hangzhou, China, 2003, 4892: 287. |
| [1] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
| [2] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [4] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [5] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [6] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [7] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
| [8] |
ZHANG Yue1, 3, ZHOU Jun-hui1, WANG Si-man1, WANG You-you1, ZHANG Yun-hao2, ZHAO Shuai2, LIU Shu-yang2*, YANG Jian1*. Identification of Xinhui Citri Reticulatae Pericarpium of Different Aging Years Based on Visible-Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3286-3292. |
| [9] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
| [10] |
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*. Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2596-2600. |
| [11] |
LENG Jun-qiang, LAN Xin-yu, JIANG Wen-shuo, XIAO Jia-yue, LIU Tian-xin, LIU Zhen-bo*. Molecular Fluorescent Probe for Detection of Metal Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2002-2011. |
| [12] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
| [13] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
| [14] |
ZHANG Qi-yan1, YANG Jie2, 3, LI Jian-guo1*, SHI Wei-xin1, GAO Peng-xin1. Research on Quantitative Identification of Rock Color Using
Spectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1905-1911. |
| [15] |
ZHENG Zhi-jie1, LIN Zhen-heng1, 2*, XIE Hai-he2, NIE Yong-zhong3. The Method of Terahertz Spectral Classification and Identification for Engineering Plastics Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1387-1393. |
|
|
|
|
