Joint Source-and-Channel Coding for Small Satellite Applications

TL;DR

This paper introduces JSCC-Sat, which applies joint source-and-channel coding using neural networks to provide efficient and robust transmission of compressed image data for satellite applications and demonstrates that it outperforms the existing approaches when applied to Earth observation data of the Sentinel-2 mission.

Abstract

Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multi-spectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low bandwidth, and high packet loss probabilities. In this paper, we introduce JSCC-Sat, which applies joint source-and-channel coding using neural networks to provide efficient and robust transmission of compressed image data for satellite applications. We evaluate our mechanism against traditional transmission schemes with separate source and channel coding and demonstrate that it outperforms the existing approaches when applied to Earth observation data of the Sentinel-2 mission.

Figures (9)

• Figure 1: An example image from the Sentinel-2 mission showing the region of Vojvodina, Serbia (Credit: processed by ESA, CC BY-SA 3.0 IGO).
• Figure 2: Comparison between the traditional communication model using separate coding and our joint coding approach.
• Figure 3: Encoder-decoder neural network architecture overview.
• Figure 4: Residual block architectures used in the encoder and decoder parts of the neural network architecture.
• Figure 5: snr values for different elevation angles.