Anomaly detection based on spatio-temporal sparse representation and visual attention analysis

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.

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Joint Sparse Representation and Multitask Learning for Hyperspectral Anomaly Detection

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323657 ◽

2020 ◽

Author(s):

Yuxiang Zhang ◽

Kai He ◽

Yanni Dong ◽

Ke Wu ◽

Tao Chen

Keyword(s):

Anomaly Detection ◽

Sparse Representation ◽

Multitask Learning ◽

Joint Sparse Representation

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Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽

10.3390/s21093099 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3099

Author(s):

V. Javier Traver ◽

Judith Zorío ◽

Luis A. Leiva

Keyword(s):

Visual Attention ◽

Computational Models ◽

Temporal Evolution ◽

Temporal Consistency ◽

Visual Salience ◽

Temporal Dimension ◽

Spatial Perspective ◽

Spatio Temporal ◽

Scoring Algorithms ◽

Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

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A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽

10.3390/s18113627 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3627 ◽

Cited By ~ 1

Author(s):

Yi Zhang ◽

Zebin Wu ◽

Jin Sun ◽

Yan Zhang ◽

Yaoqin Zhu ◽

...

Keyword(s):

Anomaly Detection ◽

Sparse Representation ◽

Parallel Algorithm ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Low Rank ◽

Detection Methods ◽

Matrix Computations ◽

Hyperspectral Image Processing ◽

Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

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