Abstract Recent advances in Artificial Intelligence (AI) and Cyber-Physical Systems (CPS)
for aerospace applications have brought about new opportunities for the fast-growing …

With the increasing volume of high-resolution satellite images, image compression
technology has become a research hotspot in the field of remote sensing image processing; …

The growing amount of data collected by Earth Observation (EO) satellites requires new
processing procedures able to manage huge quantity of information. Artificial intelligence …

When it comes to image compression in digital cameras, denoising is traditionally performed
prior to compression. However, there are applications where image noise may be necessary …

Multiresolution deep learning approaches, such as the UNet architecture, have achieved
high performance in classifying and segmenting images. Most traditional convolutional …

Satellite data transmission is a crucial bottleneck for Earth observation applications. To
overcome this problem we propose a novel solution that trains a neural network on board …

We describe an end-to-end trainable neural network for satel-lite image compression. The
proposed approach builds upon an image compression scheme based on variational auto …

In low earth orbit (LEO) satellite-based applications (eg, remote sensing and surveillance), it
is important to efficiently transmit collected data to ground stations (GS). However, LEO …

One of the main limitations to the adoption of deep learning for image compression is the
need to train multiple models to compress at multiple rates. In the case of onboard remote …

Geoinformation from satellite images is used for a variety of earth science applications.
Because of the limitations of optics and sensor technology and the high cost of Earth …