Using time series to improve endmembers estimation on multispectral images for snow monitoring

Autor: M. Dalla Mura, Marie Dumont, T. Masson, Jocelyn Chanussot
Přispěvatelé: Laboratoire de Physique Théorique d'Orsay [Orsay] (LPT), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Science et Ingénierie des Matériaux et Procédés (SIMaP ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Rok vydání: 2017
Předmět:
Zdroj: IGARSS
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2017, Fort Worth, France. ⟨10.1109/IGARSS.2017.8126937⟩
Popis: We propose to use the temporal coherence of a time series to extract using Vertex Component Analysis (VCA) the suitable set of endmembers for each scene. The reconstruction error computed on the two previous scenes for each date is used to constrain the selection of the set of endmembers produced by VCA. Snow cover estimation is considered as application. We tested different approaches for abundance estimation (FCLSU, SUnSAL, ELMM) over the French Alps from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results shows a decrease of the false positive rate with the proposed approach.
Databáze: OpenAIRE