Deep Neural Networks for automatic extraction of features in time series satellite images
This work addresses the problem of generating accurate land cover maps for earth observation applications, but it is incremental as it builds on existing neural network methods.
The authors tackled land cover classification from satellite time series images by combining a fully convolutional neural network with a convolutional LSTM, resulting in increased accuracy for producing up-to-date maps.
Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.