Title: Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks,
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.