|
|
|
|
|
|
Extraction of Plant Abnormal Information in Mining Area Based on Hyperspectral |
CUI Shi-chao1, 2, 3, ZHOU Ke-fa1, 2*, DING Ru-fu4 |
1. Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2. Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. China Non-Ferrous Metals Resource Geological Survey, Beijing 100012, China
|
|
|
Abstract Seriphidium terrae-albae is a kind of plant widely distributed in various mining areas of Fuyun County, Xinjiang, China. The traditional exploration methods are difficult to play a role due to the existence of plant information and other obstacles, and some new methods and new ideas are urgently needed. The remote sensing plant geochemistry method is a kind of natural information source that smartly utilizes plants, transforming the plant from the barrier information to the useful information. Help people quickly and economically obtain the useful information about minerals under plant barriers. Because of its large area, being fast and non-destructive and other advantages, it has attracted more and more attention of scholars, and has become the current research hotspot. In recent years, although some scholars have synthetically considered “absorption coefficient” and “contrast coefficient” to prove that Seriphidium terrae-albae can be used as a good indicator for the exploration of concealed deposits. The plants in the upper part of the deposit can absorb the ore-forming elements in the soil better, but at the same time they form geochemical anomalies in their bodies, and the information is more visible than other plant anomalies. However, no one has studied whether the geochemical anomalies in Seriphidium terrae-albae can be found from the spectral point of view, then providing some references for the exploration of concealed deposits. Therefore, our study first tries to look for the feature bands or eigenvalues closely related to geochemical anomalies, and then construct the prediction model of hidden deposit based on plant spectrum. First, the method adopted was to measure the reflectance spectra of plants growing in the upper part of deposit and background area by ASD FieldSpec3 spectrometer respectively. Then the spectra of plants growing in these two regions were analyzed and compared from five aspects, including the original spectrum, the first derivative spectrum, the second derivative spectrometry, the first derivative fractal dimension and the second derivative fractal dimension. Finally the 10 characteristic bands that were notably different were selected including R′824, R′834, R′1 533, R′1 573, R′1 633, R′1 643, R″1 284, R″1 703, the first derivative fractal dimension and the second derivative fractal dimension. These characteristic bands can be used as botanogeochemistry marks for seeking exploration of concealed deposits. Taking these ten optimized bands as input parameters, random forest (RF) and partial least squares support vector machine (PLS-SVM) were used to construct a prediction model that seeks the position of hidden deposits based on abnormal spectrum of plant. The results showed that these two models can obtain satisfactory results, but compared with the random forest model, the partial least squares support vector machine model has a better robustness and stronger generalization ability. The results also indicated that it has great potential in looking for hidden deposit using extraordinary spectrum of plants, due to the advantages of being simple and quick. Our team has built a “very low altitude detection platform” using dynamic delta wing and HySpex imaging hyperspectral sensor, which can realize the observation of “sub-meter”. But the problems will be our next research focus as follows, how to effectively solve the problem of “spatial scale” and “spectral scale”? How to better apply the model established on the ground test ground to the very low altitude detection platform, and how to extract the plant anomaly information in a large area and quickly in the research area?
|
Received: 2017-12-11
Accepted: 2018-05-05
|
|
Corresponding Authors:
ZHOU Ke-fa
E-mail: zhoukf@ms.xjb.ac.cn
|
|
[1] Rathod P H, Brackhage C, Meer F D V D, et al. European Journal of Remote Sensing, 2015, 48(3): 283.
[2] Sridhar B B, Vincent R K, Roberts S J, et al. International Journal of Applied Earth Observation & Geoinformation, 2011, 13(4): 676.
[3] Hede A N H, Kashiwaya K, Koike Katsuaki, et al. Remote Sensing of Environment, 2015, 171: 83.
[4] Zhang C, Ren H, Qin Q, et al. Remote Sensing Letters, 2017, 8(6): 576.
[5] Zhang B, Wu D, Zhang L, et al. Environmental Earth Sciences, 2012, 65(3): 649.
[6] Shi T, Liu H, Chen Y, et al. Journal of Hazardous Materials, 2016, 308: 243.
[7] Liu M L, Liu X N, Wu M X, et al. Computers & Geosciences, 2011, 37: 1642.
[8] Wang J J, Wang T J, Shi T Z, et al. Remote Sensing, 2015, 7: 15340.
[9] WANG Hui, ZENG Lu-sheng, SUN Yong-hong, et al(王 慧, 曾路生, 孙永红, 等). Transactions of the Chinese Society of Agriculture Engineering(农业工程学报), 2017, 33(2): 171.
[10] ZHU Ye-qing, QU Yong-hua, LIU Su-hong, et al(朱叶青, 屈永华, 刘素红, 等). Journal of Remote Sensing(遥感学报), 2014, 18(2): 335.
[11] Chi G Y, Shi Y, Chen X, et al. Advanced Materials Research, 2012, 347: 2735.
[12] Asmaryan S, Warner T A, Muradyan V, et al. Remote Sensing Letters, 2013, 4(2): 200.
[13] SONG Ci-an, SONG Wei, DING Ru-fu, et al(宋慈安, 宋 玮, 丁汝福, 等). Geotectonica et Metallongenia(大地构造与成矿学), 2017, 41(1): 122.
[14] YOU Jun, HONG Tao, WU Chu, et al(游 军, 洪 涛, 吴 楚, 等). Acta Petrologica Sinica(岩石学报), 2016, 32(5): 1262.
[15] QIN Ke-zhang, TIAN Ye, YAO Zhuo-sen, et al(秦克章, 田 野, 姚卓森, 等). Geology in China(中国地质), 2014, 41(3): 912.
[16] Ge W, Jiao Y Q, Sun B L, et al. South African Journal of Botany, 2012, 83(4): 98.
[17] Caldelas C, Bort J, Febrero A. Cell Biology and Toxicology, 2012, 28(1): 57.
[18] Filippidis A, Papastergios G, Kantiranis N et al. Chemie der Erde-Geochemistry, 2012, 72(1): 71. |
[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] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
|
|
|
|