Large Scale Crop Classification from Multi-temporal and Multi-spectral Satellite Images

Abstract

Crop classification is one of the foremost and most challenging applications of remote sensing. Crops exhibit both high intra-class variance across geographical locations, as well as low inter-class variance especially across seasons. As such, they require both spectral and temporal input, both of which are provided by the Sentinel 2 satellites. In this paper, we present the preliminary results of our multispectral and multitemporal crop classification analysis, on a region-wide scale, encompassing multiple climatological conditions and a high number of crop types. We have experimented using the ground-truth provided by the Farmer Registration System, with both well-known spectral and spatial shallow features and classifiers, at both pixel and field level, as well as with state of the art 3D convolutional neural networks. Our results show that Sentinel 2 imagery exhibit a strong potential as input for a systematic crop classification infrastructure.