Reduction of false alarms caused by background boundaries in real time subspace RX anomaly detection

Kanaev, Andrey; Allman, Eric; Murray-Krezan, Jeremy

doi:10.1117/12.817838

Cited by 7 publications

(3 citation statements)

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“…In addition, the size of the sliding dual window is hard to choose, while too large a window will induce contamination from anomalies, and too small a window cannot ensure the accuracy of the background description. For this, different local background models were proposed to decrease the influence of the windows size, such as the single local area [9], [10] and multi-local area (MSAD) [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, the diversity of ground objects in the background increases the difficulty of the global background description. Many solutions have been proposed to simplify the complexity of the global model, such as the multivariate normal-based anomaly detector (MVN) [14], the cluster-based anomaly detector (CBAD) [15], [16] and subspace model-based anomaly detector (SSM) [11]. The MVN hypothesizes that the background consists of different materials, and the spectral character of every material obeys the Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

2020

IEEE Access

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Anomaly detection based on clustering is a classic method that supplies a simplified manner to describe a cluttered background. However, traditional clustering methods need to know the number of clusters in advance and attempt to classify all the background pixels at one time. In addition, compared with large background clusters, small clusters are hard to discriminate due to their small populations. In this paper, an anomaly detection method based on adaptive background extraction is proposed. We apply an unsupervised clustering method to determine the cluster centers according to only the similarity of the spectral signature. To reduce the influence of the population, we propose to extract background clusters iteratively. Every iteration, we only cluster the larger clusters and extract them from the data-set. In the next iteration, the remaining pixels are clustered again. Without interference from the larger clusters, the centers of smaller clusters will appear obviously. The clustering process stop when the number of remaining pixels nears the appearance probability of anomaly (generally approximately 10%∼20%). Then, only anomalies and few background pixels remain to test. Finally, every extracted background cluster, as a viewer, is applied to measure the anomaly salience of the test pixels. In addition, a weighted summation is proposed to fuse the different salience values from different viewers. Simulation experiments on two sets of real data are presented to demonstrate the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

2020

IEEE Access

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“…Both too large window and too small window can't estimation the feature of background accurately [3]. So there were some strategies were proposed such as subspace background estimation(MSAD) [4] and random selected background set (RSAD) [5,6], which selected background set by fused multi-subset or random selected subset.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired anomaly target detection of multi-spectral remote sensing data

Zhang

Fan

et al. 2013

MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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Aimed to the limitation of present anomaly detection algorism under clutter background for multi-spectral remote sensing data, especially for the situations of dense spread target and exist different attributive of background objects, a bio-inspired anomaly detection algorithm was proposed. Simulate the information processing and fusion mechanism of fly multi-apertures vision system, multi-level background model was proposed to analysis and describe feature of clutter background. Then the threshold value can be chose adaptively according to the level of background model. The proposed algorithm didn't need the prior knowledge about anomaly, and avoids the choosing of the background widow size. A fusion mechanism was proposed to fuse the different detection results with different level background model. Simulation experiment validated the effectiveness of proposed method.

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Anomaly detection based on quadratic modeling of hyperspectral imagery

Zhong

Zhang

2016

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Reduction of false alarms caused by background boundaries in real time subspace RX anomaly detection

Cited by 7 publications

References 0 publications

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Bio-inspired anomaly target detection of multi-spectral remote sensing data

Anomaly detection based on quadratic modeling of hyperspectral imagery

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