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