Object-Based Cereal Crop Mapping Using Sentinel-2 Imagery

Abstract

This study investigates the possibilities of improving the cereal crop mapping on high spatial resolution images by using object-based approach, superpixel segmentation and the Gray-Level Co-occurrence Matrix (GLCM) texture. The proposed approach was implemented on Google Earth Engine (GEE) which provides a fast and easy-to-use platform with its freely available datasets and geospatial analysis tools for applications such classification. In this study, Multispectral Instrument (MSI) images of Sentinel-2 were utilized to classify cropland area of Sidi Bel Abbes city. The obtained results were validated by ground truth samples relative to cereal growing period (may 2021).