光谱学与光谱分析 |
|
|
|
|
|
The Spectral Prediction of Original Primary Pigment Based on Constrained Non-Negative Matrix Factorization |
HE Song-hua1, CHEN Qiao1*, DUAN Jiang2 |
1. School of Communication, Shenzhen Polytechnic, Shenzhen 518055, China 2. Computer Science Department of Southwestern University of Finance and Economics, Chengdu 610075, China |
|
|
Abstract With direct prediction in the spectral reflectance space with principal component analysis, the numbers of eigenvectors will surpass the numbers of real primary pigments while the eigenvectors and the corresponding coefficients have negative value, which can not directly presented original primary pigment spectral characteristics and corresponding concentration. We proposed an innovative spectral prediction method in which a complete linear spectral space was created according to optical properties of originals pigment. A constrained non-negative matrix factorization algorithm to predict the numbers and spectral curve shapes of real primary pigments was used in the space. So, this paper designed an overall research plan and implementation process about spectral prediction method firstly, and studied how to select and establish a spectral linear space which was conformed to optical properties of originals; taking transparent pigments as example, and spectra constrained non-negative matrix factorization (SCNMF) algorithm was established to predict primary pigment spectra based on basic non-negative matrix factorization algorithm (BNMF). Aiming at realizing multiple optimal solution of BNMF and improving the prediction accuracy as well as make the matrix decomposition results to be clearly physically meaningful; the proposed SCNMF needs to satisfy four constraints: non negative constraint, additive constraint, smoothness constraint and sparseness constraint. The objective function and iterative algorithm to meet four constraints were set up. The prediction results show that the proposed method can realize accurate prediction of original primary pigments’ numbers and spectra effectively.
|
Received: 2015-08-03
Accepted: 2015-11-16
|
|
Corresponding Authors:
CHEN Qiao
E-mail: qiaochen@szpt.edu.cn
|
|
[1] HE Song-hua, CHEN Qiao, DUAN Jiang(何颂华, 陈 桥, 段 江). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(6): 1459. [2] KONG Ling-wang, ZHU Yuan-hong, LI Qiong(孔令罔, 朱元泓, 李 琼, 等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2006, 31(9): 788. [3] WANG Ying, ZENG Ping, WANG Yi-feng(王 莹,曾 平,王义峰). Acta Optica Sinica(光学学报), 2009, 29(8): 2122. [4] LI Jin-cheng, LIU Zhen, CHEN Guang-xue(李金城, 刘 真, 陈广学). Acta Optica Sinica(光学学报), 2009, 29(8): 2122. [5] Xu Dongbo, Wang Xiangzhao, Bu Yang. Chinese Optics Letters, 2012, 10(12): 121202-1. [6] XU Fa-qiang, WAN Xiao-xia, ZHU Yuan-hong(许法强, 万晓霞, 朱元泓). Optics and Precision Engineering(光学 精密工程), 2008, 16(3): 518. [7] HE Song-hua, LIU Zhen, CHEN Qiao(何颂华, 刘 真, 陈 桥). Acta Optica Sinica(光学学报), 2014, 34(2): 0233001-1. [8] Wang Ying, Zeng Ping, Luo Xuemei, et al. Journal of Southeast University·English Edition, 2013, 2(4): 486. [9] Tzeng D Y, Berns R S. Color Research and Application, 2005, 30(2): 84. [10] He Songhua, Liu Zhen. Procedia Engineering, 2011, 23: 320. [11] Heinz D C, Chang C I. IEEE Geoscience and Remote Sensing Letters, 2001, 39(3): 529. [12] Lee D, Seung H. Nature, 1999, 401: 788. |
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[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] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|