Unsupervised Cross-Temporal Classification of Hyperspectral Images With Multiple Geodesic Flow Kernel Learning

With the increasing acquisition ability of hyperspectral remote sensing images, unsupervised cross-temporal classification (UCTC) of hyperspectral images (HSIs) has attracted more and more attention. In this paper, we focus on cross-temporal HSI classification, i.e., using one labeled HSI to classify the other unlabeled HSI. A multiple geodesic flow kernel learning (MGFKL) framework is proposed to exploit both spatial and spectral features for UCTC with bitemporal HSIs and called S2-MGFKL. The proposed S2-MGFKL method first extracts extended multi-attribute profiles (EMAPs) from the original bitemporal HSIs. The spatial features of the bitemporal HSIs obtained by the same attribute filter are paired up, so are the original spectral features. Second, each pair of features from both source and target domains are used to construct multiple geodesic flows. According to the original definition of GFK, we can obtain the construction of Gaussian base GFKs. The base kernels consist of two parts, the spectral part is obtained base on the same geodesic flow (which is constructed on the bitemporal spectral features) by tuning the kernel scale, while the spatial part is obtained under the same kernel scale but different geodesic flows constructed on different spatial feature pairs. After that, the mean rule is adopted to acquire the combined kernel, which is fed into the supervised vector machine (SVM) to implement the cross-temporal classification task. Experiments are conducted on two real HSI data sets, and the results compared with several well-known methods demonstrate the effectiveness of the proposed method.