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.

10papers

Papers

May 17, 2025

April 5, 2025

Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
Yongchuan Cui, Jinhe Zhang, Peng Liu, Weijing Song, Yi Zeng
Chinese Academy of Sciences●University of Chinese Academy of Sciences●China University of Geosciences (Wuhan)●Beijing Forestry University
Background Estimation Self Supervised Model Hyperspectral Anomaly Detection Regularization Model Anomaly Detection Self Supervised Hyperspectral

March 7, 2025

Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection
Quan Yu, Yu-Hong Dai, Minru Bai
Hunan University●Academy of Mathematics and Systems Science●University of Chinese Academy of Sciences
Hyperspectral Anomaly Detection Group Sparse Temporal Redundancy Low Rank Tensor

December 16, 2024

Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders
Seyfal Sultanov, James P Buban, Robert F Klie
Hyperspectral Anomaly Detection Energy Loss Spectroscopy 3D Convolutional

August 13, 2024

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

April 20, 2024

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

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
Meaningful Representation LOw Rank Detection Performance Hyperspectral Anomaly Detection Interpretable Network Benchmark Datasets Deep Unfolding Data Detection

January 2, 2024

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

July 19, 2023

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

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 Sample Anomaly Detection 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 Semantic Anomaly Detection Hyperspectral Anomaly Detection Development Activity Critical Lesson

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 Hyperspectral Anomaly Detection Projection Robust Wasserstein Based Anomaly Detection

Hyperspectral Anomaly Detection

Papers

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection

CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection

Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection

Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection

Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders

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