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1. .., .., .. // , 2010. 1. . 148-158.
2. ... .. - - . - .: , 2007. - 192 .
3. .. . - .: , 1976. - 328 .
. .. : . . - .: , 2007. - 283 .
5. .. - . - .: , 1999. - 480 .
6. .., .. . - .: , 1965. - 524 .
Method correlation recognition of observed objects from multispectral data
Koz'nov IA, garry-spb@yandex.ru, Pochivalov S.G., Ph.D., psg50@rambler.ru, Topchiy P.N., pavel_topchy@yandex.ru, St. Petersburg, Russia
Abstract
In recent years there has been a significant increase in interest in aerospace multispectral opto-electronic systems (MOES) observations as to the means of remote sensing (DZZ.) Modern space systems opto-electronic surveillance is widely used airborne multispectral and hyperspectral optical-electronic apparatus hav'ng up to hundreds of spectral channels in the v'sible and IK_diapazonah where multispectral recorded images. Using multispectral and hyperspectral data observation allows recognition of observed objects on the spectral features, the possibility of separation and treatment depend on the spectral resolution observations of OEA. These characteristics are determined by absorbance, emissivity and reflectivity of the observed objects in the analyzed range of electromagnetic waves. For observation data obtained by such promising optoelectronic multispectral airborne surveillance EAD are encouraged to use the most efficient in solving the problems of recognition of correlation- extreme methods such as a method of digital correlation processing of spectral images of the observed objects . Due to the fundamental possibility of automating the process of recognition of the correlation on the spectral images ( featured ) technology multispectral observations is of great interest to promptly solve the problems of observation . In space systems multispectral observations based on the use of the correlation of the observed object recognition algorithm for spectral images of the entire cycle of observation and recognition on the shooting to the decision point of belonging to the observed object to a particular class is automated, which makes such systems perspective for a broad class of problems of observation .
Keywords: multi-spectral opto-electronic system, remote sensing spectral image correlation recognition.
References
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