Comparison of Unsupervised Vegetation Classification Methods From VHR Images After Shadows Removal by Innovative Algorithms

Abstract

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.

Land Use Classification From VHR Aerial Images Using Invariant Colour Components and Texture

“…Observing the highest classification results for the various images, they show that L*a*b* performed five times over twelve as the best classifier vector, as in 2008 imagery, but also C1C2C3 based methods achieved similar results, particularly in 2011 images. This can be explained by the fact that 2011 images contain a larger amount of shadows than the corresponding 2008 ones, and the component C3 is particularly suited to detect them, as reported in shadow detection literature (Duan et al, 2013;Movia et al, 2015). In any case, also when CIE-Lab, RGB decorr., and CIE-Luv were not the best ones in the tests, their average distance from them ranges only from 2.1% to 3.7%.…”

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

“…Several studies have highlighted the advantages of remote sensing in long-term monitoring projects, focusing on different aspects, as some researchers used spectral measurements derived from numerous satellite sensors, such as vegetation indices (VIs), to evaluate vegetation coverage [ 26 , 27 , 28 ]. Other studies have focused on tracking the seasonal dynamics of vegetation [ 29 ], detecting vegetation and land changes [ 30 ], and assessing ecological revegetation and restoration projects [ 31 , 32 , 33 , 34 , 35 , 36 ].…”

Study on Shadow Detection From High-Resolution Satellite Images Using Color Model

“…However, since usually, such knowledge is not available, most of the detection algorithms are based on shadow properties methods. The property-based approaches make use of certain shadow properties in images, such as brightness, spectral characteristics, and geometry which are directly deduced from the image data information [2,3]. Because of their simplicity both in principle and implementation, the property-based approaches have been widely used in literature.…”