LSTM AutoEncoders Applied to Semi-Supervised Crop Classification

Abstract

Since creating labelled data in the field of remote sensing requires time and manpower, it has become important to use unlabelled data. In this paper we study a semi supervised long short term memory autocoder approach for crop classification with multi-temporal remote sensing data. In this study, a long short-term memory autoencoder network was trained with unlabelled data and the learned weights were used for the initialization of a model trained for classification with labelled data. The challenging Breizhcrops time series dataset, and multitemporal images of the Sakarya region were used for validation. It has been observed that the network trained with unlabelled data outperformed the network trained with only labelled data. The results shed light on the potential for unlabelled data use in the crop classification field.