Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) focuses on identifying objects or regions in hyperspectral imagery that deviate significantly from their surroundings, a crucial task in various fields like remote sensing and surveillance. Recent research emphasizes improving the robustness and generalizability of HAD methods, exploring both unsupervised approaches like low-rank representation (LRR) and supervised techniques, often integrated within deep learning frameworks such as autoencoders and unfolded LRR models. These advancements aim to address challenges posed by complex backgrounds, limited training data, and the need for accurate detection of novel anomalies, ultimately enhancing the reliability and applicability of HAD across diverse scientific and practical domains.

Papers

August 13, 2024

Investigation of unsupervised and supervised hyperspectral anomaly detection
Mazharul Hossain, Aaron Robinson, Lan Wang, Chrysanthe Preza
Comprehensive Investigation Unsupervised Method Hyperspectral Data Hyperspectral Unmixing Hyperspectral Sensor Hyperspectral Anomaly Detection

April 20, 2024

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
Yidan Liu, Weiying Xie, Kai Jiang, Jiaqing Zhang, Yunsong Li, Leyuan Fang
Hyperspectral Image Hyperspectral Datasets Self Supervised Anomaly Hyperspectral Anomaly Detection

February 23, 2024

Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
Chenyu Li, Bing Zhang, Danfeng Hong, Jing Yao, Jocelyn Chanussot
Data Detection Meaningful Representation Benchmark Datasets Deep Unfolding Detection Performance LOw Rank Interpretable Network Hyperspectral Anomaly Detection

January 2, 2024

Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector
Jitao Ma, Weiying Xie, Yunsong Li
Hyperspectral Image Spectral Feature Target Detection Small Target Detection Human Visual Perception Human Vision Hyperspectral Anomaly Detection

July 19, 2023

BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
Jitao Ma, Weiying Xie, Yunsong Li, Leyuan Fang
Hyperspectral Image Background Suppression Hyperspectral Anomaly Detection

March 31, 2023

You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, Wei An
Random Masking Hyperspectral Anomaly Detection General Anomaly Enhancement Network

March 22, 2023

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
Jingtao Li, Xinyu Wang, Shaoyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong
Anomaly Detection Anomaly Sample Hyperspectral Anomaly Detection One Stage Detection

February 14, 2023

Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
Austin P. Wright, Peter Nemere, Adrian Galvin, Duen Horng Chau, Scott Davidoff
Anomaly Detection Development Activity Critical Lesson Semantic Anomaly Detection Hyperspectral Anomaly Detection

March 18, 2022

Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection
Shizhen Chang, Pedram Ghamisi
Joint Representation Hyperspectral Anomaly Detection Anomaly Representation

December 20, 2021

Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection
Yurong Chen, Hui Zhang, Yaonan Wang, Q. M. Jonathan Wu, Yimin Yang
Anomaly Detection Variational Autoencoder Based Anomaly Detection Hyperspectral Anomaly Detection Projection Robust Wasserstein

Hyperspectral Anomaly Detection

Papers

Investigation of unsupervised and supervised hyperspectral anomaly detection

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection

Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector

BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection

You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework

Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection

Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection