Deep learning for compression and quantization of SAR data

Abstract

This chapter explores the application of deep learning techniques for the compression and quantization of SAR raw and focused data, addressing the growing challenges posed by next-generation SAR systems. The increasing resolution and coverage of SAR missions generate vast data volumes, necessitating efficient onboard data reduction methods. Traditional quantization techniques, such as Block-Adaptive Quantization (BAQ), have been widely used, but they lack adaptability to varying scene conditions. Recent advancements in AI-driven methods, particularly using convolutional neural networks (CNNs) and autoencoders, provide novel solutions to dynamically optimize bitrate allocation and improve compression performance. In this scenario, AI-based SAR data compression, although in its early stages, holds significant potential for enhancing future SAR missions by enabling more efficient data storage, transmission, and real-time applications.