Unsupervised radiometric change detection from synthetic aperture radar images - Equipe Image, Modélisation, Analyse, GEométrie, Synthèse
Communication Dans Un Congrès Année : 2024

Unsupervised radiometric change detection from synthetic aperture radar images

Résumé

Change detection is an important data processing task in remote sensing, with applications such as deforestation monitoring or natural disaster assessment. Synthetic Aperture Radar (SAR) imaging offers key advantages for change detection, in particular due to its robustness to weather condition and cloud coverage. Because of the speckle phenomenon, the intensity of SAR images suffer from strong fluctuations, making the detection of radiometric changes challenging. Our method builds on a recently introduced self-supervised despeckling technique. It estimates despeckling uncertainty to better identify meaningful differences between two despeckled images. Conformal prediction permits to approach the change detection problem from the angle of anomaly detection. Thus, we develop a fully unsupervised change detection approach with a controlled probability of false alarm. Experimental results on TerraSAR-X satellite images with metric resolution show the capability of our method to detect changes without any supervision.
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Dates et versions

hal-04683910 , version 1 (02-09-2024)

Identifiants

  • HAL Id : hal-04683910 , version 1

Citer

Thomas Bultingaire, Inès Meraoumia, Christophe Kervazo, Loïc Denis, Florence Tupin. Unsupervised radiometric change detection from synthetic aperture radar images. 32nd European Signal Processing Conference EUSIPCO 2024, Aug 2024, Lyon, France. ⟨hal-04683910⟩
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