In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to the powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies in original data for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the corresponding distributions of the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on four real data sets to evaluate the performance of our proposed method. Experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods.
Beijing Institute of Technology（北京理工大学）；Space star technology co., LTD（航天恒星科技有限公司）
In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection. Hence fine spectra of anomalous pixels can be effectively screened out from heterogeneous background pixels. Since the same materials have similar characteristics in spatial and spectral dimension, detection performance can be significantly enhanced by jointing spatial and spectral information. In this paper, a spectralspatial fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. First, original s…
Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
Central China Normal University（华中师范大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Anomaly detection (AD) has been an active research area in various domains. Yet, the increasing data scale, complexity, and dimension turn the traditional methods into challenging. Recently, the deep generative model, such as the variational autoencoder (VAE), has sparked a renewed interest in the AD problem. However, the probability distribution divergence used as the regularization is too strong, which causes the model cannot capture the manifold of the true data. In this paper, we propose the Projected Sliced Wasserstein (PSW) autoencoder-based anomaly detection method. Rooted in the optima…
ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line Scanning
School of Aerospace, Mechanical, and Mechatronic Engineering, The University of Sydney（悉尼大学航空航天、机械与机电工程学院）；School of Environmental and Rural Science, The University of New England（新英格兰大学环境与农村科学学院）；Defence Science and Technology Group（国防科学技术集团）；Australian Research Council (ARC) Training Centre for CubeSats, UAVs & Their Applications (CUAVA)（澳大利亚研究委员会立方体卫星、无人机及其应用培训中心）
Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection
Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所（IARAI））；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗赖贝格资源技术研究所）
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；School of Geography and Information Engineering, China University of Geosciences (Wuhan)（中国地质大学（武汉）地理与信息工程学院）；School of Computer Science, China University of Geosciences (Wuhan)（中国地质大学（武汉）计算机学院）；College of Information, Beijing Forestry University（北京林业大学信息学院）
Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectra…
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Indian Institute of Information Technology, Sri City（斯里城印度信息技术学院）；Indian Statistical Institute（印度统计研究所）
Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Network (CNN) is one of the most frequently used deep learning based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2D CNN. Whereas, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have utilized the 3D CNN because of increased computational complexity. This le…
Army Research Laboratory（美国陆军研究实验室）；Booz Allen Hamilton Inc.（博思艾伦汉密尔顿公司）
A pretrain-finetune strategy is widely used to reduce the overfitting that can occur when data is insufficient for CNN training. First few layers of a CNN pretrained on a large-scale RGB dataset are capable of acquiring general image characteristics which are remarkably effective in tasks targeted for different RGB datasets. However, when it comes down to hyperspectral domain where each domain has its unique spectral properties, the pretrain-finetune strategy no longer can be deployed in a conventional way while presenting three major issues: 1) inconsistent spectral characteristics among the …
Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization
School of Mathematics and Statistics, Southwest University（数学与统计学学院，西南大学）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学学院，西安交通大学）；School of Computer Science and Technology and Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China（数学科学学院/图像与视觉计算研究中心，电子科技大学）；Institute of Methodologies for Environmental Analysis, CNR-IMAA（环境分析方法学研究所，CNR-IMAA）
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Mathematics and Statistics, Southeast University（东南大学数学学院与统计学院）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、约瑟夫·傅里叶大学联合实验室）
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification
Simon Fraser University（西蒙菲莎大学）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Lancaster University（兰卡斯特大学）
Aalto University, School of Electrical Engineering（阿尔托大学电气工程学院）；University of Turin, Department of Computer Science（都灵大学计算机科学系）
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. …
School of Computer Science, Wuhan University（武汉大学计算机学院）
With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis and military reconnaissance. Different from normal change detection, hyperspectral anomaly change detection (HACD) helps to find those small but important anomaly changes between multi-temporal hyperspectral images (HSI). In previous works, most classical methods use linear regression to establish the mapping relationship between two HSIs and then detect the anomalies from the residual image. However, the real spectral di…
Sun Yat-sen University（中山大学）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；Shenzhen University（深圳大学）
Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from {hyperspectral images (HSIs)}. However, the sampling locations of the convolutional kernels of capsules are fixed and cannot be adaptively changed according to the inconsistent semantic information of HSIs. Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field…
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Key Laboratory of Computing Power Network（计算能力网络重点实验室）；Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（信息安全，教育部，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computing Power Internet（山东省计算能力互联网重点实验室）；Service Computing, Shandong Fundamental Research Center for Computer Science（服务计算，山东省计算机基础研究中心）；Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）
University of Ottawa（渥太华大学）；Queen’s University（女王大学）；National Research Council Canada（加拿大国家研究委员会）
In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-probability-based and a reconstruction-error-based normality score functions for evaluating the "outlierness" of unseen images. Our results on three datasets demonstrate that the prediction-probability-based method performs consistently well, while the reconstruction-error-based appr…
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division（计算机、电气与数学科学与工程学院）
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学光电与智能研究院）；School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学计算机学院与光电与智能研究院）
In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collaboration representation-based detector (CRD) has attracted extensive attention and in-depth research. Despite the good performance of the CRD method, its computational cost mainly arising from the sliding dual window strategy is too high for wide applications. Moreover, it takes multiple repeated tests to determine the size of the dual window, which needs to be reset once the dataset changes and cannot be identified in…
Hong Kong Baptist University（香港浸会大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University（深圳大学）；Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications…
A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner. The lesion can be found by difference between the unhealthy input and pseudo-healthy output. However, using only manually designed fake lesions fail to approximate to irregular real lesions, hence limiting the model generalization. We assume by exploring the intrinsic d…
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CASI Vision Technology CO., LTD.（卡西视觉科技有限公司）；Binzhou Institute of Technology, Weigiao-UCAS Science and Technology Park（威高-国科大科技园滨州理工学院）
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Mechanical Science & Engineering, Huazhong University of Science & Technology（华中科技大学机械科学与工程学院）
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China（教育部智能感知与图像理解重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Xidian University（西安电子科技大学）；School of Artificial Intelligence（人工智能学院）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；School of Electronic Engineering（电子工程学院）
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；school of software（软件学院）
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Utilizing pose data alleviates privacy and ethical issues. Also, computation-wise, the complexity of pose-based models is lower than pixel-based approaches. However, it introduces more challenges, such as noisy skeleton data, losing important pixel information, and not having enriched enough features. These problems are exacerbated by a lack of anomaly detection datasets that are good enough representatives of real-world scenarios. In t…
Nanjing University of Science and Technology（南京理工大学）
Unsupervised anomaly detection is a challenging task in industrial applications since it is impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection. Specifically, to ensure that the generated forged anomaly samples are conducive to model training, the Saliency Augmentation Module (SAM) is proposed. SAM introduces a saliency map to genera…
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；Advanced Analytics Institute（高级分析研究所）；University of Technology Sydney（悉尼科技大学）
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies. This approach learns the known abnormality…
Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（上海理工大学光学电子与计算机工程学院）；School of Electronics and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Medical Artificial Intelligence Lab, The First Hospital of Hebei Medical University, Hebei Medical University（河北医科大学第一医院医学人工智能实验室）；Hangzhou Institute of Technology, xidian University（杭州职业技术学院）
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection
Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Computer Science and Technology, Xidian University（协同智能系统重点实验室、教育部、计算机科学与技术学院、西安电子科技大学）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（智能感知与图像理解重点实验室、教育部、人工智能学院、西安电子科技大学）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（协同智能系统重点实验室、教育部、西安电子科技大学）
