Deep neural networks based semantic segmentation for optical time series

Abstract

Semantic segmentation or classification for satellite image time series (SITS) is a rarely touched topic, partly due to the difficulty in having the data, but more due to the unreachable task. In this research, we propose a dataset which consists of the Landsat image time series, with the purpose of performing multi-spectral semantic segmentation. As there is no ground truth information, we used unsupervised clustering to group time series into clusters, then Long short term memory (LSTM) unit based Recurrent neural networks (RNN) has been trained. We investigate the accuracy values for our test image patches, around 40% accuracy has been achieved for the sequence classification.