Range-invariant anomaly detection applied to imaging Fourier transform spectrometry data

Borel, Christoph C.; Rosario, Dalton; Romano, João

doi:10.1117/12.930177

Cited by 9 publications

(16 citation statements)

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“…The calibration was performed using an interactive data language program that processes data in several stages, as described in Borel, Rosario, and Romano (2012) and shown in Figure 2. Intermediate results are stored so that the user can investigate the effects of various processing parameters and thus minimize memory requirements.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…Now, consider the hyperspectral cube X with K bands and spatial dimension R by C pixels and treat as a random variable (real valued, not vector valued) x ¼ X k; r; c ð Þ, where k ¼ 1; Á Á Á ; K; r ¼ 1; Á Á Á ; R; and c ¼ 1; Á Á Á ; C. Assuming that x has a finite range between x min ¼ min x ð Þ and x max ¼ max x ð Þ, notice that distributions of individual sub-blocks of data in X can be calculated using a normalized histogram operation over H bins. Borel, Rosario, and Romano (2012) showed this concept applied to distribution ratios for anomaly detection. We incorporate the binomial sampling approach of Section 3.1 with the normalized histogram ratio approach as follows:…”

Section: Repeated-sampling Fusion and Detectormentioning

confidence: 98%

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Data processing and temperature-emissivity separation for tower-based imaging Fourier transform spectrometer data

Borel

Rosario

Romano

2015

International Journal of Remote Sensing

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In this article we describe the end-to-end processing of image Fourier transform spectrometry data taken at Picatinny Arsenal in New Jersey with the long-wave hyperspectral camera manufactured by the Telops company. The first part of the article discusses the processing from raw data to calibrated radiance and emissivity data. Data were taken during several months under different weather conditions every 6 min from a 213 ft-high tower of surrogate tank targets for a project sponsored by the Army Research Laboratory in Adelphi, Maryland. The second part discusses environmental effects such as diurnal and seasonal atmospheric and temperature changes and the effect of cloud cover on the data. To test the effect of environmental conditions, a range-invariant anomaly detection approach is applied to calibrate radiance, brightness temperature, and emissivity data. The data set presented in this article is due to be released publicly so that more detailed studies can be performed not only on the manmade targets but also on the changes of natural background of vegetation and gravel with time of day and seasons.

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Section: Analysis Methodsmentioning

confidence: 99%

Section: Repeated-sampling Fusion and Detectormentioning

confidence: 98%

Data processing and temperature-emissivity separation for tower-based imaging Fourier transform spectrometer data

Borel

Rosario

Romano

2015

International Journal of Remote Sensing

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“…For details on the method depicted in Fig. 3, where QTES denotes Quick Temperature and Emissivity Separation, are shown in [4]. (The method depicted in Fig.…”

Section: Lwir Hyperspectral Sensor and Characterizationmentioning

confidence: 99%

“…These settings are critical in order to achieve acceptable data quality results, daytime or nighttime. As of March 2014, the SPICE database contains over 25,000 uncalibrated LWIR hyperspectral data cubes, where approximately 70% of them have been radiometrically calibrated at ARL, using calibration code by Borel et al [4]; 100% calibration rate is expected by July 2014. We performed a basic assessment of the LWIR Hyper-Cam used in SPICE for a period of 30 days at AFIT before the data collection started at ARDEC, and compared basic performance against another specs-similar imager.…”

Section: Lwir Hyperspectral Sensor and Characterizationmentioning

confidence: 99%

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First observations using SPICE hyperspectral dataset

Rosario

Romano

Borel³

2014

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX

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Our first observations using the longwave infrared (LWIR) hyperspectral data subset of the Spectral and Polarimetric Imagery Collection Experiment (SPICE) database are summarized in this paper, focusing on the inherent challenges associated with using this sensing modality for the purpose of object pattern recognition. Emphases are also put on data quality, qualitative validation of expected atmospheric spectral features, and qualitative comparison against another dataset of the same site using a different LWIR hyperspectral sensor. SPICE is a collaborative effort between the Army Research Laboratory, U.S. Army Armament RDEC, and more recently the Air Force Institute of Technology. It focuses on the collection and exploitation of longwave and midwave infrared (LWIR and MWIR) hyperspectral and polarimetric imagery. We concluded from this work that the quality of SPICE hyperspectral LWIR data is categorically comparable to other datasets recorded by a different sensor of similar specs; and adequate for algorithm research, given the scope of SPICE. The scope was to conduct a long-term infrared data collection of the same site with targets, using both sensing modalities, under various weather and non-ideal conditions. Then use the vast dataset and associated ground truth information to assess performance of the state of the art algorithms, while determining performance degradation sources. The expectation is that results from these assessments will spur new algorithmic ideas with the potential to augment pattern recognition performance in remote sensing applications. Over time, we are confident the SPICE database will prove to be an asset to the wide open remote sensing community.

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The discrete Fourier transformation for seasonality and anomaly detection of an application to rare data

Jackson

Lacey

2020

DTA

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PurposeThe discrete Fourier transformation (DFT) has been proven to be a successful method for determining whether a discrete time series is seasonal and, if so, for detecting the period. This paper deals exclusively with rare data, in which instances occur periodically at a low frequency.Design/methodology/approachData based on real-world situations is simulated for analysis.FindingsCycle number detection is done with spectral analysis, period detection is completed using DFT coefficients and signal shifts in the time domain are found using the convolution theorem. Additionally, a new method for detecting anomalies in binary, rare data is presented: the sum of distances. Using this method, expected events which have not occurred and unexpected events which have occurred at various sampling frequencies can be detected. Anomalies which are not considered outliers to be found.Research limitations/implicationsAliasing can contribute to extra frequencies which point to extra periods in the time domain. This can be reduced or removed with techniques such as windowing. In future work, this will be explored.Practical implicationsApplications include determining seasonality and thus investigating the underlying causes of hard drive failure, power outages and other undesired events. This work will also lend itself well to finding patterns among missing desired events, such as a scheduled hard drive backup or an employee's regular login to a server.Originality/valueThis paper has shown how seasonality and anomalies are successfully detected in seasonal, discrete, rare and binary data. Previously, the DFT has only been used for non-rare data.

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Range-invariant anomaly detection applied to imaging Fourier transform spectrometry data

Cited by 9 publications

References 0 publications

Data processing and temperature-emissivity separation for tower-based imaging Fourier transform spectrometer data

Data processing and temperature-emissivity separation for tower-based imaging Fourier transform spectrometer data

First observations using SPICE hyperspectral dataset

The discrete Fourier transformation for seasonality and anomaly detection of an application to rare data

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