LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION - Télécom Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION

Résumé

This paper presents a semi-guided method to detect lakes in Sentinel-1 SAR data. The proposed approach is an adaptation of the grab-cut framework developed in [1]. Starting from a coarse bounding box around the lake, an accurate segmentation is extracted using a Conditional Random Field formalism and a graph-cut based optimization. Then an extension of this approach to process jointly a stack of multi-temporal data is presented. A temporal regularization term is introduced to control the joint segmentation. The proposed approach is evaluated on Sentinel-1 datasets. Qualitative and quantitative results demonstrate the interest of the proposed framework and its robustness to the initialization polygon of the lake.
Fichier principal
Vignette du fichier
IGARSS2022_GRABCUT_SAR_2DplusT-2.pdf (1.95 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03756052 , version 1 (22-08-2022)

Identifiants

  • HAL Id : hal-03756052 , version 1

Citer

Nicolas Gasnier, Loïc Denis, Roger Fjørtoft, Frédéric Liege, Florence Tupin. LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION. IGARSS, 2022, Kuala Lumpur, Malaysia. ⟨hal-03756052⟩
62 Consultations
60 Téléchargements

Partager

Gmail Facebook X LinkedIn More