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The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about\udenvironmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the\udclassification of the image components and for the extraction of the needed information.\udThe aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental\udbiochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been\udexperimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned\udby the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of\udthem are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based\udon RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based\udon Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called “not-centered\udoblique Procrustes” and “anisotropic Procrustes” methods to efficiently restore brightness with respect to a linear correlation\udcorrection based on the Cholesky decomposition.\udSome experimental results obtained by different classification methods after shadows removal carried out with the innovative\udalgorithms are presented and discussed

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

COMPARISON OF UNSUPERVISED VEGETATION CLASSIFICATION METHODS FROM VHR IMAGES AFTER SHADOWS REMOVAL BY INNOVATIVE ALGORITHMS