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Compressed learning based onboard semantic compression for remote sensing platforms

Bhattacharjee, Protim und Jung, Peter (2024) Compressed learning based onboard semantic compression for remote sensing platforms. In: Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024. SPIE Remote Sensing 2024, 2024-09-16 - 2024-09-19, Edinburgh, UK. ISBN 978-151068100-2. ISSN 0277-786X.

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Kurzfassung

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. Satellites are mostly used for EO and the data archive for the Sentinel missions alone exceed 77.52 PB [1] and is increasing. Bottleneck for such high throughput acquisition is the downlink bandwidth. Data-centric solutions to image compression is required to address this deluge. Though compute on remote platforms have increased significantly with the use of commercial-off-the-shelf (COTS) components, however, minimizing resource utilization is still a highly important design criterion. In this work, instead of focusing on the more common encoder-decoder architectures, we study semantic compression through a compressed learning framework [2] that utilizes only fast and sparse matrix-vector multiplication to encode the data. We consider camera noise and a communication channel as the sources of distortion. The complete semantic communication pipeline then consists of a learnt 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 a partially-unrolled network and then fed to a deep learning model performing the necessary downstream tasks; image classification and semantic segmentation are studied. Distortions are compensated by unrolling few layers of NA-ALISTA [3] with a wavelet sparsity prior. Decoding is thus a plug-n-play approach designed according to the camera/environment information and downstream task. The GLODISMO [4] framework is used for end-to-end training of the pipeline. The deep learning model for the downstream task is jointly fine-tuned with the compression matrix through the loss function. We show that addition of a recovery loss along with the task dependent losses improves the downstream performance in noisy settings. We compare our work with other encoder-decoder based approaches for semantic compression through evaluation on remote sensing datasets. References: [1] https://dashboard.dataspace.copernicus.eu/#/service-insight [2] E. Zisselman, A. Adler and M. Elad, “Compressed learning for image classification: a deep neural network approach,” in Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 1, Elsevier, 2018. [3] F. Behrens, J. Sauder and P. Jung, “Neurally augmented ALISTA”, International conference on learning representations (ICLR), 2021. [4] J. Sauder, M. Genzel and P. Jung, "Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery," in IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 3, pp. 481-492, Sept. 2022.

elib-URL des Eintrags:https://elib.dlr.de/206154/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Compressed learning based onboard semantic compression for remote sensing platforms
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Bhattacharjee, Protimprotim.bhattacharjee (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Jung, Peterpeter.jung (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:19 November 2024
Erschienen in:Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
Name der Reihe:Proceedings of SPIE - The International Society for Optical Engineering
ISSN:0277-786X
ISBN:978-151068100-2
Status:veröffentlicht
Stichwörter:compressed sensing, semantic compression, remote sensing
Veranstaltungstitel:SPIE Remote Sensing 2024
Veranstaltungsort:Edinburgh, UK
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:16 September 2024
Veranstaltungsende:19 September 2024
Veranstalter :SPIE
HGF - Forschungsbereich:keine Zuordnung
HGF - Programm:keine Zuordnung
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:Digitalisierung
DLR - Forschungsgebiet:D IAS - Innovative autonome Systeme
DLR - Teilgebiet (Projekt, Vorhaben):D - SKIAS
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Optische Sensorsysteme > Echtzeit-Datenprozessierung
Hinterlegt von: Bhattacharjee, Protim
Hinterlegt am:11 Dez 2024 11:20
Letzte Änderung:22 Jan 2025 14:42

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