Learning a joint manifold with global-local preservation for multitemporal hyperspectral image classification

Adapting a pre-trained classifier with labeled samples from an image for classification of another temporally related image is a common multitemporal image classification strategy. However, the adaptation is not effective when the spectral drift exhibited in temporal data is significant. Instead of iteratively redefining classifier parameters, we exploit similar data geometries of temporal data and project temporal data into a joint manifold space where similar samples are clustered. The proposed classification framework is based on aligning global temporal data manifolds. In addition to global structures, we also consider the local scale by incorporating local point relations into the alignment process. In experiments with challenging temporal hyperspectral data, the proposed framework provides favorable classification results, compared to the baseline.