Determination of the layer included in an interference coating that maximally influences correspondence of the spectral reflectance curve of the fabricated coating to the synthesized coating reflectance

2018 ◽  
Vol 85 (3) ◽  
pp. 182
Author(s):  
Ngo Thai Phi ◽  
Pham Van Khoa ◽  
L. A. Gubanova
Keyword(s):  
Spectral Reflectance ◽  
Reflectance Curve ◽  
Interference Coating

scholarly journals Determination of water stress with spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis

2013 ◽  
Vol 100 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Levent Genc ◽  
Melis Inalpulat ◽  
Unal Kizil ◽  
Mustafa Mirik ◽  
Scot E. Smith ◽  
...  
Keyword(s):  
Zea Mays ◽  
Water Stress ◽  
Spectral Reflectance ◽  
Zea Mays L ◽  
Sweet Corn ◽  
Classification Tree ◽  
Ct Analysis

Identification of Geospatial Objects Using Spectral Pattern

2019 ◽  
pp. 882-893
Author(s):  
Subhabrata Barman
Keyword(s):  
Spectral Reflectance ◽  
Target Surface ◽  
Classification Rules ◽  
Spectral Image ◽  
Incident Wavelength ◽  
Reflectance Curve ◽  
Spectral Pattern ◽  
Multispectral Remote Sensing ◽  
Unique Signature ◽  
Absorption Characteristics

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

Spectral Reflectance as a Rapid Technique for Field-Determination of Soil Engineering Properties

GEO-Volution ◽  
2006 ◽  
Author(s):  
Alexander F. H. Goetz ◽  
Harold. W. Olsen ◽  
David C. Noe ◽  
James R. Koehler ◽  
Jessica P. Humble ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Engineering Properties ◽  
Rapid Technique

Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

2014 ◽  
Vol 5 (2) ◽  
pp. 38-49
Author(s):  
Claudio Kapp Junior ◽  
Eduardo Fávero Caires ◽  
Alaine Margarete Guimarães
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Biomass Production ◽  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Wheat Crop ◽  
Nitrogen Status ◽  
Artificial Neural ◽  
N Rates

Precision Agriculture has the goal of reducing cost which is difficult when it is related to fertilizers application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizers application present significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the Resilient Propagation and Backpropagation Artificial Neural Networks algorithms. As a result was detected a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants. For both algorithms it was established a satisfactory model for estimating wheat dry biomass production. The best Backpropagation and Resilient Propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

Identification of Geospatial Objects Using Spectral Pattern

2017 ◽  
pp. 1-15
Author(s):  
Subhabrata Barman
Keyword(s):  
Spectral Reflectance ◽  
Target Surface ◽  
Classification Rules ◽  
Spectral Image ◽  
Incident Wavelength ◽  
Reflectance Curve ◽  
Spectral Pattern ◽  
Multispectral Remote Sensing ◽  
Unique Signature ◽  
Absorption Characteristics

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using ANN Algorithms

2018 ◽  
pp. 156-172
Author(s):  
Claudio Kapp Jr. ◽  
Eduardo Fávero Caires ◽  
Alaine Margarete Guimarães
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Fertilizer Application ◽  
Wheat Crop ◽  
Nitrogen Status ◽  
Propagation Models ◽  
Dry Biomass ◽  
N Rates

Precision agriculture has the goal of reducing cost which is difficult when it is related to fertilizer application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizer application presents significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the resilient propagation and backpropagation artificial neural networks algorithms. As a result, a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants was detected. For both algorithms a satisfactory model for estimating wheat dry biomass production was established. The best backpropagation and resilient propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

Sign in / Sign up

Export Citation Format

Share Document