Identification of Agricultural Parcels using Optical and Synthetic Aperture Radar Data

Abstract

Data fusion methodologies have been implemented in agricultural applications with different types of sensors. One of the problems in delineating cultivation areas is the mixture of spectral signatures due to the transitions between the types of cultivation, built areas, and other natural covers. In order to improve discrimination and identification of crop types, structure data fusion techniques were evaluated. This article aims at showing the potential of using satellite data from the European Space Agency, both optical and SAR, in order to improve land cover classification of agricultural land located in Mexico. To achieve this, an analysis of the spectral, spatial and textural data was performed. Specifically, two classification algorithms were used and compared. The first is based on vector support machines and the second one on Random Forests. The methodology was applied for the study of 4 types of crops in 2017 in the municipality of Villa de Arriaga located in the state of San Luis Potosí. As final results, maps were obtained with the areas with a kappa greater than 0.80.