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Joint Energy-based Modelling for Remote Sensing Image Processing

Orozco, Daniel and Liu, Chenying and Albrecht, Conrad M and Zhu, Xiao Xiang (2023) Joint Energy-based Modelling for Remote Sensing Image Processing. HelmholtzAI annual conference, 2023-06-12 - 2023-06-14, DESY Hamburg, Germany.

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Official URL: https://eventclass.it/haic2023/scientific/online-program/session?s=S-04a#e112


The peta-scale, continuously increasing amount of publicly available remote sensing information forms an unprecedented archive of Earth observation data. Although advances in deep learning provide tools to exploit big amounts of digital information, most supervised methods rely on accurately annotated sets to train models. Access to large amounts of high-quality annotations proves costly due to the human labor involved. Such limitations have been studied in semi-supervised learning where unlabeled samples aid the generalization of models trained with limited amounts of labeled data. The Joint Energy-based Model (JEM) is a recent, physics-inspired approach simultaneously optimizing a supervised task along with a generative process to train a sampler approximating a data distribution. Although a promising formulation of such models, current JEM implementations are predominantly applied to classification tasks. Their potential improving semantic segmentation tasks remains locked. Our work investigates JEM training behavior from a conceptual perspective, studying mechanisms of loss function divergences that numerically destabilizes the model optimization. We explore three regularization terms imposed on energy values and optimization gradients to alleviate the training complexity. Our experiments indicate that the proposed regularization mitigates loss function divergences for remote sensing imagery classification. Regularization on energy values of real samples performed the best. Additionally, we present an extended definition of JEM for image segmentation, sJEM. In our experiments, the generation branch did not perform as expected. sJEM was unable to generate realistic remote-sensing-like samples. Correspondingly performance is biased for the sJEM segmentation branch. Initial model optimization runs demand additional research to stabilize the methodology given spatial auto-correlations in remote sensing multi-spectral imagery. Our insights pave the way for the design of follow-up research to advance sJEM for Earth observation.

Item URL in elib:https://elib.dlr.de/195497/
Document Type:Conference or Workshop Item (Speech)
Title:Joint Energy-based Modelling for Remote Sensing Image Processing
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Liu, ChenyingUNSPECIFIEDhttps://orcid.org/0000-0001-9172-3586137359067
Albrecht, Conrad MUNSPECIFIEDhttps://orcid.org/0009-0009-2422-7289UNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDhttps://orcid.org/0000-0001-5530-3613UNSPECIFIED
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:joint energy modelling, semi-supervised learning, Earth observation analytics, deep learning
Event Title:HelmholtzAI annual conference
Event Location:DESY Hamburg, Germany
Event Type:national Conference
Event Start Date:12 June 2023
Event End Date:14 June 2023
Organizer:Helmholtz Association
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Albrecht, Conrad M
Deposited On:22 Jun 2023 13:47
Last Modified:24 Apr 2024 20:55

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