Convolutional Neural Networks for Agricultural Land Use Classification from Sentinel-2 Image Time Series

Land use classification (LUC) is the process of providing information on land cover and the types of human activity involved in land use. In this study, we perform agricultural LUC using sequences of multispectral reflectance Sentinel-2 images taken in 2018. LUC can be carried out using machine or deep learning techniques. Some existing models process data at the pixel level, performing LUC successfully with a reduced number of images. Part of the pixel information corresponds to multispectral temporal patterns that, despite not being especially complex, might remain undetected by models such as random forests or multilayer perceptrons. Thus, we propose to arrange pixel information as 2D yearly fingerprints so as to render such patterns explicit and make use of a CNN to model and capture them. The results show that our proposal reaches a 91% weighted accuracy in classifying pixels among 19 classes, outperforming random forest by 8%, or a specifically tuned multilayer perceptron by 4%. Furthermore, models were also used to perform a ternary classification in order to detect irrigated fields, reaching a 97% global accuracy. We can conclude that this is a promising operational tool for monitoring crops and water use over large areas.

Automated summary

Land use classification (LUC) is the process of providing information on land cover and the types of human activity involved in land use. This study uses multispectral reflectance Sentinel-2 images to perform agricultural LUC, achieving high accuracy by arranging pixel information as 2D yearly fingerprints and utilizing CNN for modeling and capturing multispectral temporal patterns. This operational tool shows promising potential for monitoring crops and water use over large areas.