The classification process support algorithms of shooting hyperspectral data, realizing objects' identification of the Earth's surface by means of their hyperspectral features' analysis, received from the processed space images with application of various similarity measures, are considered. Identification algorithms on the base of Euclidean distance similarity measure, angular similarity measure and fuzzy similarity measure are considered. The use expediency of fuzzy linear regression in the algorithm of objects' hyperspectral features' identification is shown. Results of hyperspectral information processing with using of the offered algorithms are presented.
Abstract. The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented. Page layoutThe problem of analysis of the hyperspectral information formed on the base of the hyperspectral images of the Earth's surface is one of the actual problems solved by the remote sensing systems. The russian "Resource-P" spacecrafts No. 1 -3 with the hyperspectral equipment on the board give out the hyperspectral image (HSI) in the form of pictures in 130 narrow adjoining ranges of visible area of spectrum. During the HSI processing much attention is paid to the questions of the objects identification on the base of their hyperspectral features (HSF).The object's HSF in a graphic form can be presented as the relationship mapping between the wavelength and the values of the spectral brightness coefficient (SBC), or the spectral reflection coefficient (SRC) of the analyzed object. However, at the solution of the problem of the objects identification on the base of their HSF the use of the SRC dependence on wavelength is more preferable because the brightness feature doesn't depend on the shooting conditions in such degree as the SBC. Besides, unlike the SBC, for obtaining the SRC values the standard reflecting surface in sight of the analyzed object isn't necessary.In this paper we suggest to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. Herewith, we test the different approaches to application of the SVM algorithm to development of the SVM classifier: the basic SVM classifier, the two-level classifier, the SVM ensemble. All of them apply the modified PSO algorithm to find the type of the kernel function, the values of the parameters of the kernel function and the value of the regularization parameter of the SVM classifier. Also, the SVM ensemble uses the principle of maximum decorrelation to choose the individual SVM classifier, which will be included into the SVM ensemble.The SVM algorithm (Support Vector Machines, SVM) is successfully used for development of the SVM classifiers for a wide range of the classification problems [1]. The SVM classifier uses the special kernel function to construct a hyperplane separating the classes of data. The SVM classifier is used for training and testing. The satisfactory quality of training and testing allows using the resulting SVM classifier in the classification of new objects.Choosing the optimal parameters values for the SMV classifier is a significant problem at the moment. It is necessary to find the kernel function type, values of the kernel function parameters and the value of the re...
Филиал акционерного общества «РКЦ «Прогресс» -ОКБ «Спектр», г. Рязань В статье рассматривается подход к решению задачи идентификации объектов земной поверхности по данным гиперспектральной съёмки от космических комплексов, базирующийся на сравнении гипер-спектральных характеристик исследуемых объектов с набором эталонных сигнатур. Предлагаются алго-ритмы идентификации объектов с использованием теории нечётких множеств: алгоритм идентификации на основе нечёткой линейной регрессии и алгоритм консолидации результатов различных решений по идентификации. Алгоритм на основе нечёткой линейной регрессии базируется на применении несиммет-ричных треугольных нечётких чисел. Данный подход, использовавшийся ранее при решении задач ап-проксимации и оценки уникальности фрагментов электронной карты, применяется для идентификации гиперспектральных характеристик. Такой выбор основан на том, что нечёткая линейная регрессия позво-ляет провести идентификацию в условиях неоднозначности. Приводятся результаты экспериментальных исследований предлагаемых алгоритмов на основе реальных данных гиперспектральной съёмки (с кос-мического аппарата «Ресурс-П» №1) в объёме 10 снимков. Показано повышение надёжности идентифи-кации при консолидации результатов от алгоритмов на основе меры подобия евклидова расстояния, уг-ловой меры подобия, а также нечётких мер подобия на 6,1 % по сравнению с одним из исходных алго-ритмов, дающим лучшее решение.Идентификация объектов, гиперспектральная характеристика, гиперспектральная съёмка, не-чёткая линейная регрессия, алгоритм.
Abstract. The identification algorithms, which carry out the objects' identification of the Earth's surface by means of their hyperspectral features' analysis, received on the base of the processed space images from the "Resource-P" spacecrafts with application of the similarity measures, have been considered. The identification algorithms on the base of the Euclidean distance similarity measure, the angular similarity measure and the fuzzy similarity measure have been applied. The use expediency of the fuzzy linear regression in the algorithm of objects' hyperspectral features' identification has been shown. The results of the hyperspectral information processing with using of the offered algorithms have been presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.