Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …

Recently, the sparse representation (SR)-based graph embedding method has been
extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold …

Linear discriminant analysis (LDA) is a popular technique for supervised dimensionality
reduction, but with less concern about a local data structure. This makes LDA inapplicable to …

The rich spectral information provided by hyperspectral imaging has made this technology
very useful in the classification of remotely sensed data. However, classification of …

In the last few years the scientific community has realized that obtaining a better
understanding of interactions between natural systems and the man-made environment …

Trajectory-User Linking (TUL), which links trajectories to users who generate them, has
been a challenging problem due to the sparsity in check-in mobility data. Existing methods …

One of the most critical devices in an electrical system is the transformer. It is continuously
under different electrical and mechanical stresses that can produce failures in its …

The advance of GPS and Wi-Fi enabled mobile devices and the enhancing ability of system
in collecting information, more and more spatio-temporal trajectory data has been collected …

As the global population increases, we face increasing demand for food and nutrition.
Remote sensing can help monitor food availability to assess global food security rapidly and …

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of
land cover that benefit from developments in spectral imaging and space technology. The …