Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images

In this study, we examine the potential of leveraging self-supervised learning (SSL) and transfer learning methodologies for forest disturbance mapping using Earth Observation (EO) data. Our focus is on natural disturbances caused by windthrow and snowload damages. Particularly, we investigate the p...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής

Κύριοι συγγραφείς:	Rdvan Salih Kuzu, Oleg Antropov, Matthieu Molinier, Corneliu Octavian Dumitru, Sudipan Saha, Xiao Xiang Zhu
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	IEEE 2024-01-01
Σειρά:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Θέματα:	Boreal forest change detection Sentinel-1 Sentinel-2 windthrown forest snowload damage
Διαθέσιμο Online:	https://ieeexplore.ieee.org/document/10418469/
Ετικέτες:	Προσθήκη ετικέτας Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!