CN106568736B - 一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 - Google Patents
一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 Download PDFInfo
- Publication number
- CN106568736B CN106568736B CN201610958574.9A CN201610958574A CN106568736B CN 106568736 B CN106568736 B CN 106568736B CN 201610958574 A CN201610958574 A CN 201610958574A CN 106568736 B CN106568736 B CN 106568736B
- Authority
- CN
- China
- Prior art keywords
- minerals
- imaging
- hyperspectral
- judging
- mineral
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 55
- 235000010755 mineral Nutrition 0.000 title claims abstract description 55
- 239000011707 mineral Substances 0.000 title claims abstract description 55
- 238000003384 imaging method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 15
- WCUXLLCKKVVCTQ-UHFFFAOYSA-M Potassium chloride Chemical compound [Cl-].[K+] WCUXLLCKKVVCTQ-UHFFFAOYSA-M 0.000 title claims abstract description 7
- 239000001103 potassium chloride Substances 0.000 title claims abstract description 7
- 235000011164 potassium chloride Nutrition 0.000 title claims abstract description 7
- XAEFZNCEHLXOMS-UHFFFAOYSA-M potassium benzoate Chemical compound [K+].[O-]C(=O)C1=CC=CC=C1 XAEFZNCEHLXOMS-UHFFFAOYSA-M 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 239000000203 mixture Substances 0.000 claims abstract description 4
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000003066 decision tree Methods 0.000 claims description 9
- 230000005855 radiation Effects 0.000 claims description 9
- 238000002310 reflectometry Methods 0.000 claims description 6
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 claims description 4
- 229910052925 anhydrite Inorganic materials 0.000 claims description 4
- OSGAYBCDTDRGGQ-UHFFFAOYSA-L calcium sulfate Chemical compound [Ca+2].[O-]S([O-])(=O)=O OSGAYBCDTDRGGQ-UHFFFAOYSA-L 0.000 claims description 4
- 235000002639 sodium chloride Nutrition 0.000 claims description 4
- 239000011780 sodium chloride Substances 0.000 claims description 4
- 230000003595 spectral effect Effects 0.000 claims description 4
- PALNZFJYSCMLBK-UHFFFAOYSA-K magnesium;potassium;trichloride;hexahydrate Chemical compound O.O.O.O.O.O.[Mg+2].[Cl-].[Cl-].[Cl-].[K+] PALNZFJYSCMLBK-UHFFFAOYSA-K 0.000 claims description 3
- 239000010446 mirabilite Substances 0.000 claims description 3
- RSIJVJUOQBWMIM-UHFFFAOYSA-L sodium sulfate decahydrate Chemical compound O.O.O.O.O.O.O.O.O.O.[Na+].[Na+].[O-]S([O-])(=O)=O RSIJVJUOQBWMIM-UHFFFAOYSA-L 0.000 claims description 3
- 239000007787 solid Substances 0.000 abstract description 3
- 238000000926 separation method Methods 0.000 abstract description 2
- 239000000126 substance Substances 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 208000035240 Disease Resistance Diseases 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- KWYUFKZDYYNOTN-UHFFFAOYSA-M Potassium hydroxide Chemical compound [OH-].[K+] KWYUFKZDYYNOTN-UHFFFAOYSA-M 0.000 description 1
- GYZGFUUDAQXRBT-UHFFFAOYSA-J calcium;disodium;disulfate Chemical compound [Na+].[Na+].[Ca+2].[O-]S([O-])(=O)=O.[O-]S([O-])(=O)=O GYZGFUUDAQXRBT-UHFFFAOYSA-J 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 229940072033 potash Drugs 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- BWHMMNNQKKPAPP-UHFFFAOYSA-L potassium carbonate Substances [K+].[K+].[O-]C([O-])=O BWHMMNNQKKPAPP-UHFFFAOYSA-L 0.000 description 1
- 235000015320 potassium carbonate Nutrition 0.000 description 1
- 159000000001 potassium salts Chemical class 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
本发明属于高光谱遥感应用领域,具体公开一种步骤1,放置参考白板;步骤2,放置待区分的钾盐矿物和脉石矿物的混合物;步骤3,获取参考白板和待区分样品的成像高光谱图像;步骤4,对上述步骤3中得到的成像高光谱图像进行预处理与数据计算;步骤5,利用上述步骤4.3得到的数据计算结果进行判断,从而区处分钾盐矿物和脉石矿物。该方法有助于钾盐矿石的固态分选。
Description
技术领域
本发明属于高光谱遥感应用领域,具体公开一种地面成像高光谱区分钾盐矿物与脉石矿物的方法。
背景技术
钾是农作物生长必须的营养元素,可以提高作物的保水性和抗病能力。钾盐矿石是生产钾肥的重要原材料。通常钾盐矿石都会与硬石膏、钙芒硝、岩盐等脉石矿物混合在一起,降低了钾盐的纯度,提升了纯化成本。传统的水化学方法提纯钾盐不仅污染环境,成本也较高。
发明内容
本发明的目的在于提供一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,该方法有助于钾盐矿石的固态分选。
实现本发明目的的技术方案:一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,该方法包括如下步骤:
步骤1,放置参考白板;
步骤2,放置待区分的钾盐矿物和脉石矿物的混合物;
步骤3,获取参考白板和待区分样品的成像高光谱图像;
步骤4,对上述步骤3中得到的成像高光谱图像进行预处理与数据计算;
步骤5,利用上述步骤4.3得到的数据计算结果进行判断,从而区处分钾盐矿物和脉石矿物。
所述的步骤1中的参考白板放置在Hyspex成像高光谱仪的运行轨道下方。
所述的步骤2中将待区分的样品平铺放置在Hyspex成像高光谱仪的扫描仪的运行轨道2下方。
所述的步骤3中的具体步骤如下:
将Hyspex短波红外光谱仪安置在轨道上,启动短波红外光谱仪获取参考白板和待区分样品的成像高光谱图像。
所述的步骤4中的具体步骤如下:
步骤4.1、使用Hyspex短波红外光谱仪自带的辐射校正工具对获取的光谱图像进行辐射校正;
步骤4.2、利用ENVI软件的经验线性方法对辐射校正后的图像进行白板定标,获得反射率图像;
步骤4.3、对步骤4.2中获得的反射率图像利用ENVI软件的Band Math工具按照如下公式对反射率图像进行计算,获得b1、b2、b3、b4、b5、b6、b7、b8、b9、b10的值。
所述的步骤4中b1、b2、b3、b4、b5、b6、b7、b8、b9、b10计算公式如下:
b1=(R(λ=1120)+R(λ=1280))/(R(λ=1230));
b2=(R(λ=1320)+R(λ=1670))/(R(λ=1460));
b3=(R(λ=1680)+R(λ=1850))/(R(λ=1760));
b4=(R(λ=1850)+R(λ=2080))/(R(λ=1980));
b5=(R(λ=1300)+R(λ=1650))/(R(λ=1100));
b6=(R(λ=1850)+R(λ=2140))/(R(λ=1951));
b7=(R(λ=1140)+R(λ=1280))/(R(λ=1210));
b8=(R(λ=1300)+R(λ=1680))/(R(λ=1460));
b9=(R(λ=1700)+R(λ=1840))/(R(λ=1800));
b10=(R(λ=1840)+R(λ=2150))/(R(λ=1970))。
所述的步骤5中的,具体步骤如下:
利用ENVI软件的决策树工具进行判断,如果b1>2.14且b2>3.24且b3>2.33且b4>4.33,判断得到该矿物为光卤石型钾盐矿物;
利用ENVI软件的决策树工具进行判断,如果b5>2.25且b6>2.75,判断得到该矿物为硬石膏或盐岩型脉石矿物;
利用ENVI软件的决策树工具进行判断,如果b7>2.06且b8>3.18且b9>2.14且b10>4.09,判断得到该矿物为钙芒硝型脉石矿物。
本发明的有益技术效果:本发明的成像高光谱技术由于实现了图像信息与光谱信息的合二为一,因而不仅可以识别不同类型的物质,也可以不同类型物质的空间分布情况,为钾盐的固态分选提供的新途径。(1)由于成像光谱积分时间短,可以快速地对钾盐矿物和脉石矿物进行判别,因而具有很高的效率;(2)可以与传送带和自动分选装置结合,实现钾盐的固态分选。
具体实施方式
下面结合实施例对本发明作进一步详细说明。
本发明所提供的一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,该方法包括如下步骤:
步骤1,放置参考白板,具体步骤如下:
将参考白板放置在Hyspex成像高光谱仪的运行轨道下方;
参考白板为漫反射白板。
步骤2,放置待区分的样品,具体步骤如下:
将待区分的样品平铺放置在Hyspex成像高光谱仪的扫描仪的运行轨道2下方;
上述待区分的样品为钾盐矿物和脉石矿物的混合物。
步骤3,获取参考白板和待区分样品的成像高光谱图像,具体步骤如下:
首先将Hyspex短波红外光谱仪安置在轨道上,之后启动Hyspex短波红外光谱仪获取参考白板和待区分样品的成像高光谱图像。
步骤4,对上述步骤3中得到的成像高光谱图像进行预处理与数据计算,具体步骤如下:
步骤4.1、使用Hyspex短波红外光谱仪自带的辐射校正工具对获取的光谱图像进行辐射校正;
步骤4.2、利用ENVI软件的经验线性方法对辐射校正后的图像进行白板定标,获得反射率图像;
步骤4.3、对步骤4.2中获得的反射率图像利用ENVI软件的Band Math工具按照如下公式对反射率图像进行计算,获得b1、b2、b3、b4、b5、b6、b7、b8、b9、b10的值如下所示:
b1=(R(λ=1120)+R(λ=1280))/(R(λ=1230));
b2=(R(λ=1320)+R(λ=1670))/(R(λ=1460));
b3=(R(λ=1680)+R(λ=1850))/(R(λ=1760));
b4=(R(λ=1850)+R(λ=2080))/(R(λ=1980));
b5=(R(λ=1300)+R(λ=1650))/(R(λ=1100));
b6=(R(λ=1850)+R(λ=2140))/(R(λ=1951));
b7=(R(λ=1140)+R(λ=1280))/(R(λ=1210));
b8=(R(λ=1300)+R(λ=1680))/(R(λ=1460));
b9=(R(λ=1700)+R(λ=1840))/(R(λ=1800));
b10=(R(λ=1840)+R(λ=2150))/(R(λ=1970))。
其中,R(λ)表示反射率图像波长为λnm处的反射率值,例如R(λ=1120)代表波长为1120nm处的反射率数值。
步骤5,利用上述步骤4.3得到的数据计算结果进行判断,从而区处分钾盐矿物和脉石矿物,具体步骤如下:
利用ENVI软件的决策树工具进行判断,如果b1>2.14且b2>3.24且b3>2.33且b4>4.33,判断得到该矿物为光卤石型钾盐矿物;
利用ENVI软件的决策树工具进行判断,如果b5>2.25且b6>2.75,判断得到该矿物为硬石膏或盐岩型脉石矿物;
利用ENVI软件的决策树工具进行判断,如果b7>2.06且b8>3.18且b9>2.14且b10>4.09,判断得到该矿物为钙芒硝型脉石矿物。
上面结合实施例对本发明作了详细说明,但是本发明并不限于上述实施例,在本领域普通技术人员所具备的知识范围内,还可以在不脱离本发明宗旨的前提下作出各种变化。本发明中未作详细描述的内容均可以采用现有技术。
Claims (3)
1.一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,其特征在于,该方法包括如下步骤:
步骤1,放置参考白板;所述的步骤1中的参考白板放置在Hyspex成像高光谱仪的运行轨道下方;
步骤2,放置待区分的钾盐矿物和脉石矿物的混合物;
步骤3,获取参考白板和待区分样品的成像高光谱图像;
步骤4,对上述步骤3中得到的成像高光谱图像进行预处理与数据计算;所述的步骤4中的具体步骤如下:步骤4.1、使用Hyspex成像高光谱仪自带的辐射校正工具对获取的光谱图像进行辐射校正;
步骤4.2、利用ENVI软件的经验线性方法对辐射校正后的图像进行白板定标,获得反射率图像;
步骤4.3、对步骤4.2中获得的反射率图像利用ENVI软件的Band Math工具按照如下公式对反射率图像进行计算,获得b1、b2、b3、b4、b5、b6、b7、b8、b9、b10的值;
所述的步骤4中b1、b2、b3、b4、b5、b6、b7、b8、b9、b10计算公式如下:b1=(R(λ=1120)+R(λ=1280))/(R(λ=1230));
b2=(R(λ=1320)+R(λ=1670))/(R(λ=1460));
b3=(R(λ=1680)+R(λ=1850))/(R(λ=1760));
b4=(R(λ=1850)+R(λ=2080))/(R(λ=1980));
b5=(R(λ=1300)+R(λ=1650))/(R(λ=1100));
b6=(R(λ=1850)+R(λ=2140))/(R(λ=1951));
b7=(R(λ=1140)+R(λ=1280))/(R(λ=1210));
b8=(R(λ=1300)+R(λ=1680))/(R(λ=1460));
b9=(R(λ=1700)+R(λ=1840))/(R(λ=1800));
b10=(R(λ=1840)+R(λ=2150))/(R(λ=1970));
步骤5,利用上述步骤4得到的数据计算结果进行判断,从而区分钾盐矿物和脉石矿物,所述的步骤5的具体步骤如下:
利用ENVI软件的决策树工具进行判断,如果b1>2.14且b2>3.24且b3>2.33且b4>4.33,判断得到该矿物为光卤石型钾盐矿物;
利用ENVI软件的决策树工具进行判断,如果b5>2.25且b6>2.75,判断得到该矿物为硬石膏或盐岩型脉石矿物;
利用ENVI软件的决策树工具进行判断,如果b7>2.06且b8>3.18且b9>2.14且b10>4.09,判断得到该矿物为钙芒硝型脉石矿物。
2.根据权利要求1所述的一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,其特征在于:所述的步骤2中将待区分的样品平铺放置在Hyspex成像高光谱仪的扫描仪的运行轨道下方。
3.根据权利要求2所述的一种地面成像高光谱区分钾盐矿物与脉石矿物的方法,其特征在于:所述的步骤3中的具体步骤如下:
将Hyspex成像高光谱仪安置在轨道上,启动Hyspex成像高光谱仪获取参考白板和待区分样品的成像高光谱图像。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610958574.9A CN106568736B (zh) | 2016-10-28 | 2016-10-28 | 一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610958574.9A CN106568736B (zh) | 2016-10-28 | 2016-10-28 | 一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106568736A CN106568736A (zh) | 2017-04-19 |
CN106568736B true CN106568736B (zh) | 2020-02-21 |
Family
ID=58535785
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610958574.9A Active CN106568736B (zh) | 2016-10-28 | 2016-10-28 | 一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106568736B (zh) |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103353616A (zh) * | 2013-07-05 | 2013-10-16 | 吉林大学 | 一种基于高光谱遥感数据快速识别油气微渗漏的方法 |
CN104215584A (zh) * | 2014-08-29 | 2014-12-17 | 华南理工大学 | 一种基于高光谱图像技术区分大米产地的检测方法 |
CN105021529A (zh) * | 2015-06-11 | 2015-11-04 | 浙江水利水电学院 | 融合光谱和图像信息的作物病虫害识别和区分方法 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8406469B2 (en) * | 2009-07-20 | 2013-03-26 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | System and method for progressive band selection for hyperspectral images |
-
2016
- 2016-10-28 CN CN201610958574.9A patent/CN106568736B/zh active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103353616A (zh) * | 2013-07-05 | 2013-10-16 | 吉林大学 | 一种基于高光谱遥感数据快速识别油气微渗漏的方法 |
CN104215584A (zh) * | 2014-08-29 | 2014-12-17 | 华南理工大学 | 一种基于高光谱图像技术区分大米产地的检测方法 |
CN105021529A (zh) * | 2015-06-11 | 2015-11-04 | 浙江水利水电学院 | 融合光谱和图像信息的作物病虫害识别和区分方法 |
Non-Patent Citations (2)
Title |
---|
Using HySpex SWIR-320m hyperspectral data for the identification and mapping of minerals in hand specimens of carbonate rocks from the Ankloute Formation(Agadir Basin,Weatern Morocco);Rachid Baissa et al.;《Journal of African Earth Sciences》;20110504;第61卷;1-9 * |
高光谱遥感岩矿识别的研究进展;张成业等;《光学精密工程》;20150831;第23卷(第8期);2407-2418 * |
Also Published As
Publication number | Publication date |
---|---|
CN106568736A (zh) | 2017-04-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gracia-Romero et al. | Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization | |
Banerjee et al. | High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response | |
CN101915738B (zh) | 基于高光谱成像技术的茶树营养信息快速探测方法及装置 | |
Pisani et al. | Minor and trace elements in different honey types produced in Siena County (Italy) | |
Martin et al. | Natural variation of magnesium isotopes in mammal bones and teeth from two South African trophic chains | |
Puppe et al. | Physicochemical surface properties of different biogenic silicon structures: Results from spectroscopic and microscopic analyses of protistic and phytogenic silica | |
Gerecht et al. | High temperature decreases the PIC/POC ratio and increases phosphorus requirements in Coccolithus pelagicus (Haptophyta) | |
Fichot et al. | SeaUV and SeaUVC: Algorithms for the retrieval of UV/Visible diffuse attenuation coefficients from ocean color | |
Anderson et al. | Bauxite residue fines as an amendment to residue sands to enhance plant growth potential—a glasshouse study | |
Rigual Hernández et al. | Coccolithophore populations and their contribution to carbonate export during an annual cycle in the Australian sector of the Antarctic zone | |
Garcias-Bonet et al. | High denitrification and anaerobic ammonium oxidation contributes to net nitrogen loss in a seagrass ecosystem in the central Red Sea | |
CN104198457A (zh) | 基于光谱成像技术的烟丝组分识别方法 | |
CN101034475A (zh) | 无阴影卫星遥感正射数字图像的计算机生成方法 | |
Bøen et al. | Meat and bone meal and biosolids as slow-release phosphorus fertilizers | |
CN106568736B (zh) | 一种地面成像高光谱区分钾盐矿物与脉石矿物的方法 | |
Curra-Sánchez et al. | Contrasting land-uses in two small river basins impact the colored dissolved organic matter concentration and carbonate system along a river-coastal ocean continuum | |
Barlow et al. | Phytoplankton production and adaptation in the vicinity of Pemba and Zanzibar islands, Tanzania | |
van der Does et al. | Opposite dust grain-size patterns in the Pacific and Atlantic sectors of the Southern Ocean during the last 260,000 years | |
Ball et al. | Hyperspectral imaging predicts yield and nitrogen content in grass–legume polycultures | |
CN113588596A (zh) | 一种鱼粉中掺假动物源蛋白粉鉴别和含量检测方法及系统 | |
Li et al. | Biogeochemical characteristics of settling particulate organic matter in Lake Superior: A seasonal comparison | |
Aguirre-Villegas et al. | Nutrient variability following dairy manure storage agitation | |
Kiyashko et al. | Sulfur, carbon, and nitrogen stable isotope ratios in soft tissues and trophic relationships of fish from the near-shore waters of the peter the great bay in the Sea of Japan | |
Mendes et al. | Spatio-temporal structure of diatom assemblages in a temperate estuary. A STATICO analysis | |
Gan et al. | EVALUATION OF A SPECTROPHOTOMETRIC METHOD FOR PRACTICAL AND COST EFFECTIVE QUANTIFICATION OF FULVIC ACID. |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |