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 …

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

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

Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have
demonstrated excellent performance on hyperspectral images (HSIs). The AE can …

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a
crucial factor in anomaly detection (AD) evaluation. The difficulty in dealing with BS arises …

Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder
(DAE) models have been proven to be effective for the task of anomaly detection (AD) in …

Recently, the autoencoder (AE)-based methods in hyperspectral anomaly detection (HAD)
have attracted a lot of attention from scholars and researchers, and they acquire satisfying …

Sparse representation (SR)-based approaches and collaborative representation (CR)-
based methods are proved to be effective to detect the anomalies in a hyperspectral image …

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