Tensor Decomposition-Inspired Convolutional Autoencoders for Hyperspectral Anomaly Detection

Tensor Decomposition-Inspired Convolutional Autoencoders for Hyperspectral Anomaly Detection

Anomaly detection from hyperspectral images (HSI) is an important task in the remote sensing domain. Considering the three-order characteristics of HSI, many tensor decomposition based hyperspectral anomaly detection (HAD) models have been proposed and drawn much attention during the past decades. H...

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Bibliographic Details
Main Authors: Bangyong Sun, Zhe Zhao, Di Liu, Xiaomei Gao, Tao Yu
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Anomaly detection
hyperspectral image (HSI)
tensor decomposition network
Online Access:https://ieeexplore.ieee.org/document/9802669/

Internet

https://ieeexplore.ieee.org/document/9802669/

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