Nonlinear Time-Series Adaptation for Land Cover Classification

Adeline Bailly (1, 2) , Laetitia Chapel (2) , Romain Tavenard (2, 1) , Gustau Camps-Valls (3)

1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
2 OBELIX - Environment observation with complex imagery
3 IPL - Image Processing Laboratory

Automatic land cover classification from satellite image time series is of paramount relevance to assess vegetation and crop status, with important implications in agriculture, biofuels and food. However, due to the high cost and human resources needed to characterize and classify land cover through field campaigns, a recurrent limiting factor is the lack of available labeled data. On top of this, the bio-geo-physical variables exhibit particular temporal structures that need to be exploited. Land cover classification based on image time series is very complex because of the data manifold distortions through time. We propose the use of the kernel manifold alignment (KEMA) method for domain adaptation of remote sensing time series before classification. KEMA is nonlinear, semi-supervised, and reduces to solve a simple generalized eigenproblem. We give empirical evidence of performance through classification of biophysical (LAI, fAPAR, FVC, NDVI) time series at a global scale.

Mots clés: Land cover classification; time series; domain adaptation; manifold alignment; kernel methods

Domaines: Méthodes et statistiques

Adeline Bailly, Laetitia Chapel, Romain Tavenard, Gustau Camps-Valls. Nonlinear Time-Series Adaptation for Land Cover Classification. IEEE Geoscience and Remote Sensing Letters, 2017, ⟨10.1109/LGRS.2017.2686639⟩. ⟨halshs-01515283⟩