Using Consolidated Covariance Image for Discrimination of Habitats

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.