Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

Hyperspectral imaging can provide narrow bands and continuous spectrum information.
However, hyperspectral image data have the characteristics of high dimensionality, rich …

Several dimensionality reduction techniques were applied to hyperspectral reflectance
images of wine grape berries, leading a study of the machine learning models' efficiency in …

With the development of Earth observation technology, a very-high-resolution (VHR) image
has become an important data source of change detection (CD). These days, deep learning …

Anomaly detection is a fundamental task in hyperspectral image (HSI) processing. However,
most existing methods rely on pixel feature vectors and overlook the relational structure …

Due to the high spectral resolution, anomaly detection from hyperspectral images provides a
new way to locate potential targets in a scene, especially those targets that are spectrally …

Hyperspectral anomaly detection (AD) is an important problem in remote sensing field. It can
make full use of the spectral differences to discover certain potential interesting regions …

Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis
over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the …

This paper proposes a nonlinear version of an anomaly detector with a robust regression
detection strategy for hyperspectral imagery. In the traditional Mahalanobis distance-based …

Anomaly detection is one of the most popular applications in hyperspectral remote sensing
image analysis. Anomaly detection technique does not require any prior features or …