Deep Learning for Super-Resolution of Time Series Satellite Images

We propose to use deep learning techniques to enhance the spatial resolution of time series satellite images. This improvement, also called “super-resolution” is becoming essential to compensate for relatively low resolution sensors on resource constrained environments such as SmallSats and CubeSats. Software approaches are increasingly considered in connection with smaller satellites for which size and power constraints limit the capabilities of the sensors. Recently, deep learning techniques have been used successfully for achieving super-resolution of single hyperspectral images; we are generalizing this approach to time series satellite multispectral or hyperspectral images.