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

Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

A novel algorithm that combines sparse and collaborative representation is proposed for
target detection in hyperspectral imagery. Target detection is achieved by the representation …

In recent years, deep learning has dramatically improved the cognitive ability of the network
by extracting depth features, and has been successfully applied in the field of feature …

Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI)
processing. Recently, compressive sensing technology has been widely used in …

The detection and identification of target pixels such as certain minerals and man-made
objects from hyperspectral remote sensing images is of great interest for both civilian and …

Target detection is one of the most important applications in hyperspectral remote sensing
image analysis. However, the state-of-the-art machine-learning-based algorithms for …

For hyperspectral target detection, it is usually the case that only part of the targets pixels
can be used as target signatures, so can we use them to construct the most proper …

Whereas the transform coding algorithms have been proved to be efficient and practical for
grey-level and color images compression, they could not directly deal with the hyperspectral …

Hyperspectral target detection is an approach which tries to locate targets in a hyperspectral
image on the condition of given targets spectrum. Many classical target detectors are based …