Abundant spectral information endows unique advantages of hyperspectral remote sensing
images in target location and recognition. Target detection techniques locate materials or …

Anomaly detection is of great importance among hyperspectral applications, which aims at
locating targets that are spectrally different from their surrounding background. A variety of …

This article proposes a dual global–local attention network (DGLAnet), which is an end-to-
end unsupervised band selection (UBS) method that fully utilizes spatial and spectral …

Hyperspectral anomaly detection methods based on representation model have attracted
much attention in recent years. In the method, a background dictionary is used to represent …

Hyperspectral–multispectral image (HSI-MSI) fusion relies on a robust degradation model
and data prior, where the former describes the degeneration of HSI in the spectral and …

Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …

Deep belief networks (DBNs) have been widely applied in hyperspectral imagery (HSI)
processing. However, the original DBN model fails to explore the prior knowledge of training …

Traditional target detection methods assume that the background spectrum is subject to the
Gaussian distribution, which may only perform well under certain conditions. In addition …

Accurate estimation of crop yield before harvest, especially in early growth stages, is
important for farmers and researchers to optimize field management and evaluate crop …

The high dimensionality of a hyperspectral image (HSI) provides the possibility of deeply
capturing the underlying and intrinsic characteristics in spectra, such that targets embedded …