Compressed learning-based onboard semantic compression for remote sensing platforms

Protim Bhattacharjee; Peter Jung

doi:10.1117/12.3031472

19 November 2024 Compressed learning-based onboard semantic compression for remote sensing platforms

Protim Bhattacharjee, Peter Jung

Proceedings Volume 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX; 1319608 (2024) https://doi.org/10.1117/12.3031472
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Earth observation (EO) plays a crucial role in creating and sustaining a resilient and prosperous society that has far reaching consequences for all life and the planet itself. Remote sensing platforms like satellites, airborne platforms, and more recently dones and UAVs are used for EO. They collect large amounts of data and this needs to be downlinked to Earth for further processing and analysis. Bottleneck for such high throughput acquisition is the downlink bandwidth. Data-centric solutions to image compression is required to address this deluge. In this work, semantic compression is studied through a compressed learning framework that utilizes only fast and sparse matrix-vector multiplication to encode the data. Camera noise and a communication channel are the considered sources of distortion. The complete semantic communication pipeline then consists of a learned low-complexity compression matrix that acts on the noisy camera output to generate onboard a vector of observations that is downlinked through a communication channel, processed through an unrolled network and then fed to a deep learning model performing the necessary downstream tasks; image classification is studied. Distortions are compensated by unrolling layers of NA-ALISTA with a wavelet sparsity prior. Decoding is thus a plug-n-play approach designed according to the camera/environment information and downstream task. The deep learning model for the downstream task is jointly fine-tuned with the compression matrix and the unrolled network through the loss function in an end-to-end fashion. It is shown that addition of a recovery loss along with the task dependent losses improves the downstream performance in noisy settings at low compression ratios.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Protim Bhattacharjee and Peter Jung "Compressed learning-based onboard semantic compression for remote sensing platforms", Proc. SPIE 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX, 1319608 (19 November 2024); https://doi.org/10.1117/12.3031472

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