|
|
|
|
|
|
Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology |
WANG Xuan-hui1, 2, ZHENG Xi-lai1*, HAN Zhong-zhi2, WANG Xuan-li3, WANG Juan4 |
1. Key Lab of Marine Environmental Science and Ecology, Ministry of Education, College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Science and Information College, Qingdao Agricultural University, Qingdao 266109, China
3. Information Engineering and Automation Department, Shanxi Institute of Technology, Yangquan 045000, China
4. The Environmental Monitoring Center of North China Sea, State Oceanic Administration, Qingdao 266033, China |
|
|
Abstract In order to solve the problem of lower prediction performance caused by the difficulty in retrieving the key features from hyperspectral data of soil available potassium, this paper proposes a novel hybrid feature selection algorithm based on Random Forests. Firstly, wrapper-based feature selection methods were applied to rapidly remove the redundancies and preserve the related features. Secondly, an Improved-RF feature selection algorithm was applied to further accurately select the wavelength variables from the pre-selected feature sets. In this step, characteristic wavelength with strong robustness and discriminative could be selected through improving the dipartite degree between the key and redundant features and using an iterative feature selection method. Therefore, the problem of low prediction performance in the soil available potassium inversion model could be better solved by using our hybrid feature selection algorithm. In order to verify the validity of our algorithm, 124 representative soil samples collected from the Dagu River Basin were chosen. Using our algorithm, the optimal feature subset which contained 13 sensitive bands have been selected and used to build soil available potassium content inversion model. This work compared the model performance of full bands, current feature selection algorithms and our algorithm. The comparison results indicated that our algorithm not only selects minimum numbers of wavelength features and reduces the dimension of full bands, but also achieves better prediction performance with lower RMSEP (9.661 5), higher R (0.936 9) and RPD (2.14). As an effective method of soil available potassium inversion model, the algorithm proposed in this paper can provide theoretical basis for the design of real-time soil nutrient sensors.
|
Received: 2017-11-07
Accepted: 2018-03-19
|
|
Corresponding Authors:
ZHENG Xi-lai
E-mail: zhxilai@ouc.edu.cn
|
|
[1] Gupta S, Yadav B S, Raj U, et al. Front in Plant Science, 2017, 8(1025): 1.
[2] Ministry of Agricultural of the People’s Republic of China, NY/T 889—2004. Determination of Exchangeable Potassium and Non-Exchangeable Potassium Content in Soil(中华人民共和国农业部. NY/T 889—2004. 土壤速效钾和缓效钾含量的测定),2005.
[3] Viscarra Rossel R A, Behrens T, Ben-Dor E, et al. Earth-Science Reviews, 2016, 155: 198.
[4] Vohland M, Ludwig M, Thiele-Bruhn S, et al. Geoderma, 2014, 223-225: 88.
[5] Iznaga A C, Orozco, M R, Alcantara E A, et al. Biosystems Engineering, 2014, 125: 105.
[6] ZHANG Hai-liang, LIU Xue-mei, HE Yong(章海亮,刘雪梅,何 勇). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(5): 1348.
[7] Mehmood T, Liland K H, Snipen L, et al. Chemometricsand Intelligent Laboratory Systems, 2012, 118: 62.
[8] LIU Xue-mei, LIU Jian-she(刘雪梅, 柳建设). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(3): 88.
[9] Jia S, Yang X, Zhang J, et al. Soil Science, 2014, 179(4): 211.
[10] Ben Ishak A. Intelligent Data Analysis, 2016, 20(1): 83.
[11] Leo Breiman. Random forests. Machine Learning, 2001, 45(1): 5.
[12] Yu X, Liu Q, Wang Y B, et al. Catena, 2016, 137: 340.
[13] Wang X M, Chen Y Y, Guo L, et al. Remote Sensing, 2017, 9: 201.
[14] Li H D, Xu Q S, Liang Y Z. Peer J Prepr, 2014, 2: e190v191.
[15] Huang N T, Lu G B, Xu D G. Energies, 2016, 9: 767.
[16] Leo Breiman. Bagging Predictors. Machine Learning, 1996, 24: 123. |
[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] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[6] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[7] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[11] |
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. |
[12] |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui. A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3483-3491. |
[13] |
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. |
[14] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
[15] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
|
|
|
|