Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection

Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervisi...

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Bibliographic Details
Main Authors: Degang Wang, Longfei Ren, Xu Sun, Lianru Gao, Jocelyn Chanussot
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural networks (CNNs)
deep learning (DL)
hyperspectral images (HSIs)
self-attention
self-supervised learning
Online Access:https://ieeexplore.ieee.org/document/10890991/
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https://ieeexplore.ieee.org/document/10890991/

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