Hyperspectral anomaly detection: A survey
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 …
abundant and detailed spectral information offers a unique diagnostic identification ability for …
Interpretable anomaly detection with diffi: Depth-based feature importance of isolation forest
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …
Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder
Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings, and is an active area of research in hyperspectral image processing …
surroundings, and is an active area of research in hyperspectral image processing …
Hyperspectral anomaly detection with robust graph autoencoders
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 …
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …
Hyperspectral anomaly detection using ensemble and robust collaborative representation
S Wang, X Hu, J Sun, J Liu - Information Sciences, 2023 - Elsevier
In this paper, we propose a novel ensemble and robust anomaly detection method based on
collaborative representation-based detector. The focused pixels used to estimate the …
collaborative representation-based detector. The focused pixels used to estimate the …
Hyperspectral anomaly detection by fractional Fourier entropy
Anomaly detection is an important task in hyperspectral remote sensing. Most widely used
detectors, such as Reed-Xiaoli (RX), have been developed only using original spectral …
detectors, such as Reed-Xiaoli (RX), have been developed only using original spectral …
Hyperspectral anomaly detection with relaxed collaborative representation
Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …
abundant spectral and spatial information contained in hyperspectral images. Recently …
Hyperspectral image restoration using weighted group sparsity-regularized low-rank tensor decomposition
Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a
common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and …
common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and …
Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection
Recently, the low-rank and sparse decomposition model (LSDM) has been used for
anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse …
anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse …
Hybrid feature aligned network for salient object detection in optical remote sensing imagery
Recently, salient object detection in optical remote sensing images (RSI-SOD) has attracted
great attention. Benefiting from the success of deep learning and the inspiration of natural …
great attention. Benefiting from the success of deep learning and the inspiration of natural …