The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …

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

Recent years have witnessed the flourishing of deep learning-based methods in
hyperspectral anomaly detection (HAD). However, the lack of available supervision …

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from
the surrounding background in unlabeled hyperspectral images (HSIs). Most existing deep …

Recent years have witnessed significant advances of deep learning technology in
hyperspectral anomaly detection (HAD). Among these methods, existing unsupervised …

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects that deviate from
surrounding backgrounds in an unlabeled hyperspectral image (HSI). Most available neural …

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …

With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult
Problem in current research. The graph neural network (GNN) has emerged as an approach …

Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …

Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …