Building Light Models with Competitive Performance for Remote Sensing

The communication between ground stations and low earth orbit satellites is limited by a window of time as well as by the signal transmission speed. As a consequence, machine learning models for remote sensing need to be reasonably small in order to be transmitted and loaded to the device. Top performing deep learning models in the literature usually include millions of parameters, which limits their potential use on board once the satellite is in orbit. This paper is inspired by a previous work, PRANC, which explores the feasiblity of using a linear combination of multiple pseudo-randomly generated frozen models for classification purposes. We extend its use to semantic segmentation of building footprints. While this is not a reduction technique as such, results demonstrate that these type of models can be easily transmitted and reconstructed on board without compromising the model performance. In particular, the network reaches a competitive performance, while requiring only hundreds of kilobytes.