A support vector method for anomaly detection in hyperspectral imagery

Banerjee, Amit; Burlina, Philippe; Diehl, Christopher P.

doi:10.1109/tgrs.2006.873019

Cited by 339 publications

(152 citation statements)

References 13 publications

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“…According the author, anomaly detection aims the pattern recognition of detected objects that stand out the cluttered background. Along with some already mentioned anomaly detectors, such as Reed-Xiaoli (RX) [79] and subspace-based [84] detectors, others were highlighted as emerging approaches, more specifically, Kernel RX [85]-non-linear version of RX detector to deal with the lack of Gaussian distribution behavior of clutter background, which cannot be easily modelled due to insufficient training data and knowledge about Gaussian mixtures-and support vector data description (SVDD) [86], which is a classifier capable of estimating support region for a given data set, avoiding prior information of distribution. Anomalous change detection (ACD) was defined by [87] as an algorithm capable of suppressing environmental changes to highlight materials transformation, giving a couple of hyperspectral images.…”

Section: Subpixel Target Detectionmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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Section: Subpixel Target Detectionmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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“…By analyzing the decision function of formula (24), and using formula (29) to obtain pre-image of the center of hyper-sphere, the aim is to reduce the computational complexity from O(|SVs|) to O(1) in the decision-making process of the SVDD algorithm.…”

Section: Remarkmentioning

confidence: 99%

“…SVDD can get a more flexible boundary to adapt irregularly shaped target datasets, which is able to be effectively applied to the field of anomaly detection. [21][22][23][24] However, in the training phase, SVDD is required to solve the quadratic programming problem with the strength of calculation and obtain the decision boundary of target data. If the number of training samples is M, then its computational complexity will be up to O(M 3 ).…”

Section: Introductionmentioning

confidence: 99%

A new approach of anomaly detection in wireless sensor networks using support vector data description

Zhu

et al. 2017

International Journal of Distributed Sensor Networks

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Anomaly detection is an important challenge in wireless sensor networks for some applications, which require efficient, accurate, and timely data analysis to facilitate critical decision making and situation awareness. Support vector data description is well applied to anomaly detection using a very attractive kernel method. However, it has a high computational complexity since the standard version of support vector data description needs to solve quadratic programming problem. In this article, an improved method on the basis of support vector data description is proposed, which reduces the computational complexity and is used for anomaly detection in energy-constraint wireless sensor networks. The main idea is to improve the computational complexity from the training stage and the decision-making stage. First, the strategy of training sample reduction is used to cut back the number of samples and then the sequential minimal optimization algorithm based on the second-order approximation is implemented on the sample set to achieve the goal of reducing the training time. Second, through the analysis of the decision function, the pre-image in the original space corresponding to the center of hyper-sphere in kernel feature space can be obtained. The decision complexity is reduced from O(l) to O(1) using the pre-image. Eventually, the experimental results on several benchmark datasets and real wireless sensor networks datasets demonstrate that the proposed method can not only guarantee detection accuracy but also reduce time complexity.

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“…In HSI anomaly detection, the Reed-Xiaoli (RX) detector of Reed and Yu [4] is widely used and considered a baseline algorithm [5][6][7][8][9][10][11][12]. The well-known RX detector is the benchmark algorithm derived from a generalized likelihood ratio test for an unknown additive contrast signal in a multivariate Gaussian background.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

2016

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Real-time anomaly detection has received wide attention in remote sensing image processing because many moving targets must be detected on a timely basis. A widely-used anomaly detection algorithm is the Reed-Xiaoli (RX) algorithm that was proposed by Reed and Yu. The kernel RX algorithm proposed by Kwon and Nasrabadi is a nonlinear version of the RX algorithm and outperforms the RX algorithm in terms of detection accuracy. However, the kernel RX algorithm is computationally more expensive. This paper presents a novel real-time anomaly detection framework based on the kernel RX algorithm. In the kernel RX detector, the inverse covariance matrix and the estimated mean of the background data in the kernel space are non-causal and computationally inefficient. In this work, a local causal sliding array window is used to ensure the causality of the detection system. Using the matrix inversion lemma and the Woodbury matrix identity, both the inverse covariance matrix and estimated mean can be recursively derived without extensive repetitive calculations, and, therefore, the real-time kernel RX detector can be implemented and processed pixel-by-pixel in real time. To substantiate its effectiveness and utility in real-time anomaly detection, real hyperspectral data sets are utilized for experiments.

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A support vector method for anomaly detection in hyperspectral imagery

Cited by 339 publications

References 13 publications

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

A new approach of anomaly detection in wireless sensor networks using support vector data description

Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

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