Unsupervised Temporal-Adaptation with Multiple Geodesic Flow Kernels for Hyperspectral Image Classification

Tianzhu Liu, Yanfeng Gu

Abstract: The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. Since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.