Hyperspectral sensors are passive instruments that record reflected electromagnetic
radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two …

Anomaly detection is an important task for hyperspectral data exploitation. A standard
approach for anomaly detection in the literature is the method developed by Reed and …

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures
are different from their background. Anomalies are often man-made targets. With such target …

Remotely sensed hyperspectral imaging offers the possibility to collect hundreds of images,
at different wavelength channels, for the same area on the surface of the Earth …

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of
remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) …

Spectral indices are of fundamental importance in providing insights into the distinctive
characteristics of oil spills, making them indispensable tools for effective action planning …

Window-based operation is a general technique for hyperspectral anomaly detection.
However, the problem remains that background knowledge containing abnormal information …

Fast detecting difficult targets such as subpixel objects is a fundamental challenge for
anomaly detection (AD) in hyperspectral images. In an attempt to solve this problem, this …

Anomaly detection (AD) has emerged as a prominent area of research in hyperspectral
imagery (HSI) processing. Traditional algorithms, such as low-rank and sparse matrix …

Anomaly detection is an important task for hyperspectral data exploitation. A standard
approach for anomaly detection in the literature is the method developed by Reed and Yu …