# Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks

## TL;DR

This paper evaluates the effectiveness of Recurrent Neural Networks, especially LSTM, for land cover classification using multi-temporal satellite image data, showing competitive or superior performance to traditional methods.

## Contribution

It demonstrates the potential of RNNs, particularly LSTM, for land cover classification in remote sensing, including improvements over classical classifiers and benefits of feature representations.

## Key findings

- RNNs are competitive with state-of-the-art classifiers.
- LSTM-based features can enhance standard classifier performance.
- RNNs may outperform traditional methods for rare or mixed classes.

## Abstract

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.