Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …

Deep learning (DL) has heavily impacted the data-intensive field of remote sensing.
Autoencoders are a type of DL methods that have been found to be powerful for blind …

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task,
physics-based methods have become popular because, with their explicit mixing models …

In order to address the correlation between feature maps and the context information of
spectral sequences that are not considered in the classi-fication of hyperspectral image …

The application of hyperspectral imaging with computer-aided technology has promising
prospects, and achieving real-time, efficient, and non-destructive detection, especially for …

This article proposes a blind nonlinear unmixing technique for intimate mixtures using the
Hapke model and convolutional neural networks (HapkeCNN). We use the Hapke model …

Autoencoders (AEs) are commonly utilized for acquiring low-dimensional data
representations and performing data reconstruction, which makes them suitable for …

Spectral unmixing is an effective tool to mine information at the subpixel level from complex
hyperspectral images. To consider the spatially correlated materials distributions in the …

Hyperspectral unmixing is a popular research topic in hyperspectral processing, aiming at
obtaining the ground features contained in the mixed pixels and their proportion. Recently …