Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a
crucial factor in anomaly detection (AD) evaluation. The difficulty in dealing with BS arises …

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It
aims to decompose a mixed pixel into a collection of spectrally pure components (called …

Image classification is considered to be one of the critical tasks in hyperspectral remote
sensing image processing. Recently, a convolutional neural network (CNN) has established …

Nonlinear graph-based dimensionality reduction algorithms such as Laplacian Eigenmaps
(LE) and Schroedinger Eigenmaps (SE) have been shown to be very effective at yielding …

We present Q-Seg, a novel unsupervised image segmentation method based on quantum
annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation …

Schroedinger Eigenmaps (SE) has recently emerged as a powerful graph-based technique
for semi-supervised manifold learning and recovery. By extending the Laplacian of a graph …

Classifying hyperspectral data within high dimensionality is a challenging task. To cope with
this issue, this study implements a semi‐supervised multi‐kernel class consistency …

Land cover classification of satellite images has been a very predominant area since the last
few years. An increase in the amount of information acquired by satellite imaging systems …

This paper introduces a graph Laplacian regularization in the hyperspectral unmixing
formulation. The proposed regularization relies upon the construction of a graph …

In the segmentation of hyperspectral images (HSI), difficulties arise when different objects
with similar spectral characteristics are being distinguished. If these objects with similar …