An Algorithm for an Accurate Detection of Anomalies in Hyperspectral Images With a Low Computational Complexity

Díaz, María Auxiliadora Martín; Guerra, Raúl; López, Sebastián; Sarmiento, Roberto

doi:10.1109/tgrs.2017.2761019

Cited by 39 publications

(12 citation statements)

References 24 publications

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“…The higher the AER is, the better the algorithm performs. More detailed explanation for these two metrics can be found in [ 75 ]. For a fair comparison, each detection map is linearly normalized by its maximum value in the performance evaluation step, and all parameters of each method are optimal.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Structured Background Modeling for Hyperspectral Anomaly Detection

Zhang

et al. 2018

Sensors

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Background modeling has been proven to be a promising method of hyperspectral anomaly detection. However, due to the cluttered imaging scene, modeling the background of an hyperspectral image (HSI) is often challenging. To mitigate this problem, we propose a novel structured background modeling-based hyperspectral anomaly detection method, which clearly improves the detection accuracy through exploiting the block-diagonal structure of the background. Specifically, to conveniently model the multi-mode characteristics of background, we divide the full-band patches in an HSI into different background clusters according to their spatial-spectral features. A spatial-spectral background dictionary is then learned for each cluster with a principal component analysis (PCA) learning scheme. When being represented onto those dictionaries, the background often exhibits a block-diagonal structure, while the anomalous target shows a sparse structure. In light of such an observation, we develop a low-rank representation based anomaly detection framework that can appropriately separate the sparse anomaly from the block-diagonal background. To optimize this framework effectively, we adopt the standard alternating direction method of multipliers (ADMM) algorithm. With extensive experiments on both synthetic and real-world datasets, the proposed method achieves an obvious improvement in detection accuracy, compared with several state-of-the-art hyperspectral anomaly detection methods.

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Section: Experiments and Discussionmentioning

confidence: 99%

Structured Background Modeling for Hyperspectral Anomaly Detection

Zhang

et al. 2018

Sensors

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“…In this case, the detailed window sizes for different competitors are introduced as follows. For LRX method, 4 pairs of window sizes (wout, win) containing (17,7), (17,9), (19,7), and (19,9) are used in the experiments by comprehensively taking all the spectral dimensions of the three images into consideration and simultaneously avoiding covariance matrix singular problem. As for SVDD, owing to its heavy computation burden, we finally select (13, 7), (13,9), (15,7), and (15,9).…”

Section: ) Parameter Setupmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

Yuan

Wang

2019

IEEE Access

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Hyperspectral anomaly detection is a research hot spot in the field of remote sensing. It can distinguish abnormal targets from the scene just by utilizing the spectral differences and requiring no prior information. A series of anomaly detectors based on Reed-Xiaoli methods are very important and typical algorithms in this research area, which generally have the hypothesis about background subject to the Gaussian distribution. However, this assumption is inaccurate to describe a hyperspectral image with a complex scene in practice. Besides, due to the unavoidable existence of abnormal targets, background statistics will be affected which will reduce the detection performance. To address these problems, we propose a sparse dictionary learning method by using a capped norm to realize hyperspectral anomaly detection. Moreover, a new training data selection strategy based on clustering technique is also proposed to learn a more representative background dictionary. The main contributions are concluded in threefold: 1) neither making any assumptions on the background distribution nor computing the covariance matrix, the proposed method is more adaptive to all kinds of complex hyperspectral images in practice; 2) owing to the good qualities of the capped norm, the learned sparse background dictionary is resistant to the effect of anomalies and has stronger distinctiveness to anomalies from background; 3) without using the traditional sliding hollow window technique, the proposed method is more effective to detect different sizes of abnormal targets. The extensive experiments on four commonly used real-world hyperspectral images demonstrate the effectiveness of the proposed method and show its superiority over the benchmark methods.

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“…This detector requires specifying the number of bands to use for representing the image information considered as background. This number has been set to 4 in the experiments, since this value produces very good anomaly detection results for both data sets [33]. By doing so, two anomaly maps have been obtained, one per image.…”

Section: Evaluation Of the Impact Produced By The Hyperlca Compressiomentioning

confidence: 99%

A New Algorithm for the On-Board Compression of Hyperspectral Images

et al. 2018

Self Cite

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Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth's surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

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An Algorithm for an Accurate Detection of Anomalies in Hyperspectral Images With a Low Computational Complexity

Cited by 39 publications

References 24 publications

Structured Background Modeling for Hyperspectral Anomaly Detection

Structured Background Modeling for Hyperspectral Anomaly Detection

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

A New Algorithm for the On-Board Compression of Hyperspectral Images

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