RGB2LST: Enhancing Deep Learning-Based Land Surface Temperature Estimation with Multi-Modality and Artifacts Removal - Laboratoire d'InfoRmatique en Image et Systèmes d'information Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

RGB2LST: Enhancing Deep Learning-Based Land Surface Temperature Estimation with Multi-Modality and Artifacts Removal

Résumé

Accurate Land Surface Temperature (LST) estimation is crucial for understanding environmental dynamics and addressing diverse scientific and societal challenges. This study explores a novel approach for LST estimation using RGB data and integrating it with additional data modalities. By leveraging conditioned Generative Adversarial Networks (cGANs) for LST generation on adjacent tiles, certain artifacts are observed. To address this issue, we introduce a comprehensive processing pipeline and tiling strategy, evaluating fusion methods for artifact removal and improving LST generation accuracy. Our findings demonstrate the potential of integrating multi-modal data to enhance LST estimation, leading to promising advancements, particularly in LST data inference.
Fichier principal
Vignette du fichier
Camera_ready_Eusipco2024.pdf (917.74 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04622620 , version 1 (24-06-2024)

Identifiants

  • HAL Id : hal-04622620 , version 1

Citer

Issam Khedher, Jean-Marie Favreau, Serge Miguet, Gilles Gesquière. RGB2LST: Enhancing Deep Learning-Based Land Surface Temperature Estimation with Multi-Modality and Artifacts Removal. 32nd European signal processing conference, European Association For Signal Processing, Aug 2024, Lyon, France. ⟨hal-04622620⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More