The method of decomposition of the values of two-dimensional spectral features of remote sensing based on the analysis of the components of the correlation coef cient

Abstract

A technique has been developed for decomposing the values of two-dimensional spectral features according to their components of correlation coeffi cients. A close analogue of the proposed methodology are automatic classification algorithms. The basis of the methodology is the analysis of the proposed indicator – the products of normalized values of spectral features and their probability density. A nonparametric Rosenblatt-Parzen estimate is used to reconstruct the probability density from the initial statistical data. The peculiarity of the proposed indicator and the user-selected threshold values of the indicator make it possible to form variants of the decomposition of the initial statistical data and mapping of the results obtained during the computational experiment. Using a human-machine decomposition procedure for the values of two-dimensional spectral features, it is possible to circumvent the problem of solving optimization problems when using automatic classifi cation algorithms and use information about the relationship between spectral features in the elements of the earth’s surface. The results of the application of the technique in the processing of remote sensing data of the forest area and their comparison with the initial information are considered. Spectral features have been established, which mainly determine the decomposition between dead wood and other forest conditions. The obtained results reveal their development in the formation of sets of spectral features in the assessment of the states of natural objects.