Advances in hyperspectral sensing provide new capability for characterizing spectral
signatures in a wide range of physical and biological systems, while inspiring new methods …

Hyperspectral sensors record the reflectance from the Earth's surface over the full range of
solar wavelengths with high spectral resolution. The resulting high-dimensional data contain …

The graph embedding (GE) methods have been widely applied for dimensionality reduction
of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the …

In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial
features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, ie, the …

Convolution neural networks (CNNs) and graph representation learning are two common
methods for hyperspectral image (HSI) classification. Recently, graph convolutional neural …

Graph construction, which is at the heart of graph-based semisupervised learning (SSL), is
investigated by using manifold learning (ML) approaches. Since each ML method can be …

In recent years, dimensionality reduction (DR) and classification have become important
issues of hyperspectral image analysis. In this paper, we propose a new spatial–spectral …

Distance functions are at the core of important data analysis and processing tools, eg, PCA,
classification, vector median filter, and mathematical morphology. Despite its key role, a …

Graph based manifold learning and embedding techniques have been very successful at
representing high dimensional hyperspectral data in lower dimensions for visualization and …

The paper proposes an upgraded landmark-Isometric mapping (UL-Isomap) method to
solve the two problems of landmark selection and computational complexity in …