Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …

Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.
HAD can find pixels with anomalous spectral signatures compared with their neighbor …

In recent years, researchers have paid increasing attention on hyperspectral image (HSI)
classification using deep learning methods. To improve the accuracy and reduce the training …

Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings, and is an active area of research in hyperspectral image processing …

A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on
low-rank and sparse representation. The proposed method is based on the separation of the …

In this paper, collaborative representation is proposed for anomaly detection in
hyperspectral imagery. The algorithm is directly based on the concept that each pixel in …

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI)
processing. The traditional anomaly detection methods mainly extract knowledge from the …

For the classification of hyperspectral images (HSIs), this paper presents a novel framework
to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which …

In this letter, a novel anomaly detection framework with transferred deep convolutional
neural network (CNN) is proposed. The framework is designed by considering the following …