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Nonlinear Time-Series Adaptation for Land Cover Classification

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 biophysical–geophysical 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 and semisupervised and reduces to solve a simple generalized eigenproblem. We give empirical evidence of performance through classification of biophysical (leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, and normalized difference vegetation index) time series on a global scale.