Classification of Sentinel-2 Imagery Using Rayleigh Distribution Modeling

Nowadays land cover classification from satellite imagery is one of most actual and important problems in remote sensing. Multispectral satellite images such as Sentinel-2 images provide high-resolution imagery in different spectral bands, enabling detailed distinguishing of surface objects. This study presents a method of multispectral satellite image classification based on Rayleigh distribution, maximum likelihood method and likelihood functions. It was considered three land cover classes, such as “Water”, “Vegetation”, and “Buildings”, applying three spectral bands (Red spectral band, Green spectral band and Blue spectral band). Proposed classification procedure includes modeling spectral distributions with the Rayleigh probability distribution. The Rayleigh distribution parameters for each class and each spectral band are estimated from training data via the proposed formula. The ESA SNAP software is applied for image processing. Maximum likelihood method is applied for classification procedure. In remote sensing this method is used to classify pixels in satellite imagery into different classes. This method is based on assigning each pixel to the class, for which has the highest probability of belonging. It was described the methodology, including data preparation using the ESA SNAP software and data analysis in Microsoft Excel. The mathematical formulation of the Rayleigh distribution and the mathematical algorithm of calculation of likelihood functions for each class and for each spectral band have been considered. Results include fitted Rayleigh distribution parameters for each class and for each spectral band, classification maps, calculation of likelihood functions and classification result. The classification result depends on which class the maximum likelihood function corresponds to. An example has been considered where the class “Vegetation” is determined using the maximum likelihood method and Rayleigh distribution. The proposed approach can be applied for land-cover classification, ecological monitoring, agriculture and geological tasks.