Журнал Российского общества по неразрушающему контролю и технической диагностике
The journal of the Russian society for non-destructive testing and technical diagnostic
 
| Русский Русский | English English |
 
Главная Archive
27 | 04 | 2024
2018, 02 February

DOI: 10.14489/td.2018.02.pp.060-063

 

Kerimova M. I.
OPTIMIZATION OF DERIVATIVE SPECTRAL CHARACTERISTICS OF MULTI-COLORED OBJECTS UPON REMOTE DIAGNOSTICS
(pp. 60-63)

Abstract. The authenticity of remote diagnostics of multicolored objects requires providing the true representation of all colors hues which can be quaranteed by help of modern multichannerl hyperspectrometers. The paper is devoted to research of condditions of optimization of utilization of method of second derivative upon diagnostics of multispectral objects. It is wellknown that the second derivative of spectral characteristics of multicolored objec is relatively non-sensitive for variations of illumination of researched object but sufficiently sensitive for noise signals and in such conditions 6the task of minimization of noise effect or filtration of them gains a special importance. The stressed out properties of the second derivative method, namely the capability to amplify the specific low spectral elements and their strong subjection to impact of noises make it possible to develop the information criterion to determine the optimum type of main and derivative spectral characteristics of tested object. An example for utilization of suggested croterion by way of interrelasted calculation of hyperspectral information charsacteristics asnd its second derivative using the Eyler–Lagrange equition solution of which provide for extremum of information content of severely noised hyperspectral diagnostic signals.

Keywords: remote diagnostics, multicolored object, optimization, authenticity, spectral characteristic, hyperspectrometer.

 

M. I. Kerimova (Azerbaijan State University of Oil and Industry, Baku, Azerbaijan) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.  

 

 

1. Hyperspectral imagery. Unsupervised and supervised band selection. Available at: https://isda.ncsa. illi-nois.edu/Hyperspectral/hyperAnalysis.html
2. Tsai F., Philpot W. (1998). Derivative analysis of Hyperspectral data. Remote Sensing of Environment, 66, pp. 41-51.
3. Monteiro S. T., Uto K., Kosugi Y. et al. (2008). Hyperspectral image classification of grass species in northeast Japan. IGAARSS. Paper. 978-1-4244-2808, pp. 399-402. Available at: http://wwwacademia.edu/17696653/ Hyperspecial_in_northeast_Japan
4. Louchard E. M., Reid R. P., Stephens C. F. Derivative analysis of absorption features in hyperspectral remote sensing data of carbonate sediments.
5. El'sgol'ts L. E. (1969). Differential equations and the calculus of variations. Moscow: Nauka. [in Russian language].

 

 

This article  is available in electronic format (PDF).

The cost of a single article is 350 rubles. (including VAT 18%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.

After depositing your payment on our bank account we send you file of the article by e-mail.

To order articles please copy the article doi:

10.14489/td.2018.02.pp.060-063

and fill out the  form  

 

 

 
Search
Баннер
Rambler's Top100 Яндекс цитирования