A number of methods for classification of individual trees in high resolution multispectral images have been developed. The paper provides comparative analysis of some practicable methods of such type. Classification accuracy into 5 species was tested by computer simulations with real multispectral data obtained using airborne hyperspectral sensor. Coordinates and species of individual trees were supplied for testing by field work. It is shown that classification accuracy better than 97 % can be reached by more sophisticated methods in favorable conditions. Presented results can be used to choose a classification method appropriate for the particular forest inventory task. Ill. 1, bibl. 7 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.371
In this paper the method for transforming the multispectral image is defined, based on the Cholesky decomposition of empirical covariance matrices of pixels within a chosen window and consecutive calculation of mean values of the triangular matrix elements. This procedure is called the covariance consolidation method and it is applied to three subsets of the spectral bands thus transforming p-dimensional image into the 3 dimensional Consolidated Covariance Image (CCIm). CCIm is proposed to visualize the spectral diversity of remotely sensed objects. Within the described study, CCIm was created from the 15-band multispectral image to perform visual analysis of the nature park "Dviete floodplain" in Latvia. It was shown that CCIm provides complementary information about the habitats that can be used for their discrimination. CCIm can be used together with Principal Component Analysis (PCA) or other methods to classify regions of interest.
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