Open AccessСПЕЦИФИКА ОЦЕНИВАНИЯ ХАРАКТЕРИСТИК СЛОЖНЫХ ПОМЕХ С УЧЕТОМ МЕЖКАНАЛЬНОЙ КОРРЕЛЯЦИИ НА ГИПЕРСПЕКТРАЛЬНЫХ ИЗОБРАЖЕНИЯХ
Author(s) -
Алёна Сергеевна Григорьева,
Виктория Валерьевна Абрамова,
Vladimir Lukin,
Клавдий Данилович Абрамов,
Надежда Владимировна Кожемякина
Publication year - 2018
Publication title -
avacìjno-kosmìčna tehnìka ì tehnologìâ/avìacìjno-kosmìčna tehnìka ta tehnologìâ
Language(s) - English
Resource type - Journals
eISSN - 2663-2217
pISSN - 1727-7337
DOI - 10.32620/aktt.2018.6.13
Subject(s) - stability (learning theory) , channel (broadcasting) , computer science , artificial intelligence , pattern recognition (psychology) , image processing , joint (building) , noise (video) , operability , correlation , correlation coefficient , cross correlation , mathematics , hyperspectral imaging , algorithm , image (mathematics) , statistics , machine learning , architectural engineering , computer network , geometry , software engineering , engineering
The article deals with the scatter-plot method of automatic evaluation of mixed noise characteristics in multi-channel images. The aim is to solve the problematic issues associated with the adaptation of this method to hyperspectral images processing. The tasks to be solved are: to investigate the influence of the formation method of the jointly processed multichannel groups of images on the accuracy and stability of the aforementioned method; to formulate the recommendations on the choice of jointly processed images and the method of combination. The applied methods are the following: robust estimation of signal parameters, spectral, correlation and regression analysis. The following results were obtained. Three possible ways of groups forming of three channel images were considered: 1) joint processing of images belonging to adjacent channels; 2) joint processing of images with the highest cross-correlation coefficients; 3) joint processing of images with the lowest cross-correlation coefficients. It was defined that if the cross-correlation coefficients of images in the group are low, and the images have of complex structure, it is possible a significant reduction of the method accuracy, up to a complete loss of its working capacity. The method demonstrates sufficiently high accuracy and stability when the groups are formed of the neighbor channel images or of the images with the highest cross-correlation coefficients, and the values of the estimated noise parameters for these issues have no significant differences. Conclusions. The group formation method significantly affects not only the accuracy, but also the operability of the considered estimation method, and in order to increase the reliability of the method, it is appropriate to form groups of images with rather high levels of inter-channel correlation. However, since the accuracy of the method when groups are formed of the neighbor images and of images with the highest levels of cross-correlation have no significant differences, in order to maintain the high performance of the method, it is recommended to form groups of jointly processed images applying the images obtained in the neighbor spectral zones