Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images

Abstract

Land-cover classification (LCC) is one of the central problems in earth observation and was extensively investigated over recent decades. In many cases, existing approaches concentrate on single-time and multi- or hyper-spectral reflectance measurements observed by spaceborne and airborne sensors. However, land-cover classes, such as crops, change their reflective characteristics over time, thus complicating a classification at one particular observation time. Opposed to that, these characteristics change in a systematic and predictive manner, which should be utilized in a multi-temporal approach. We employ long short-term memory (LSTM) networks to extract temporal characteristics from a sequence of SENTINEL 2A observations. We compared the performance of LSTM networks with other architectures and a support vector machine (SVM) baseline and show the effectiveness of dynamic temporal feature extraction. For our experiments, a large study area together with rich ground truth annotations provided by public authorities was used for training and evaluation. Our rather straightforward LSTM variant achieved state-of-the art classification performance, thus opening promising potential for further research.