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...

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Detalles Bibliográficos

Principais autores:	Rdvan Salih Kuzu, Oleg Antropov, Matthieu Molinier, Corneliu Octavian Dumitru, Sudipan Saha, Xiao Xiang Zhu
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Boreal forest change detection Sentinel-1 Sentinel-2 windthrown forest snowload damage
Acceso en liña:	https://ieeexplore.ieee.org/document/10418469/
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