МУИС, Монгол хүн сансарт ниссэний 35 жил_2016.pdf

Abstract: At present, the processing and analysis of hyperspectral images have become the main tasks of many researchers dealing with RS image processing. Unlike the traditional multispectral datasets taken in the optical range of electro-magnetic spectrum, the hyperspectral data deals with an enormous amount of bands and the data are formed as collections of hundreds of images of the same scene with each image corresponding to a narrow interval of the electro-magnetic wavelength. It is clear that such datasets offer the superior potential for more accurate and detailed information extraction than is possible with other types of RS data. The purpose of this paper is to classify landcover types using hyperspectral and radar images. For the feature extraction of the visible, near infrared and middle infrared bands, principal component transformation (PCT) has been applied. The outputs of the feature extraction are classified using maximum likelihood classification. The results are analyzed and compared.