Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …
(RS) imaging has provided a significant amount of spatial and spectral information for the …
Machine learning based hyperspectral image analysis: a survey
Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …
remotely for the purpose of identification, detection, and chemical composition analysis of …
LRR-Net: An interpretable deep unfolding network for hyperspectral anomaly detection
Considerable endeavors have been expended toward enhancing the representation
performance for hyperspectral anomaly detection (HAD) through physical model-based …
performance for hyperspectral anomaly detection (HAD) through physical model-based …
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 …
Learning tensor low-rank representation for hyperspectral anomaly detection
Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection
Anomaly detection is playing an increasingly important role in hyperspectral image (HSI)
processing. The traditional anomaly detection methods mainly extract knowledge from the …
processing. The traditional anomaly detection methods mainly extract knowledge from the …
Graph and total variation regularized low-rank representation for hyperspectral anomaly detection
T Cheng, B Wang - IEEE Transactions on Geoscience and …, 2019 - ieeexplore.ieee.org
Anomaly detection is of great importance among hyperspectral applications, which aims at
locating targets that are spectrally different from their surrounding background. A variety of …
locating targets that are spectrally different from their surrounding background. A variety of …
Weighted-RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery
Anomaly detection is an active topic in hyperspectral imaging, with many practical
applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses …
applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses …
A tensor decomposition-based anomaly detection algorithm for hyperspectral image
X Zhang, G Wen, W Dai - IEEE Transactions on Geoscience …, 2016 - ieeexplore.ieee.org
Anomalies usually refer to targets with a spot of pixels (even subpixels) that stand out from
their neighboring background clutter pixels in hyperspectral imagery (HSI). Compared to …
their neighboring background clutter pixels in hyperspectral imagery (HSI). Compared to …
Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery
W Sun, C Liu, J Li, YM Lai, W Li - Journal of Applied Remote …, 2014 - spiedigitallibrary.org
A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to
detect anomalies in hyperspectral imagery (HSI). The detector assumes background images …
detect anomalies in hyperspectral imagery (HSI). The detector assumes background images …