Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system

Stevenson, Brian; O'Connor, Rory; Kendall, William B.; Stocker, Alan D.; Schaff, William; Alexa, Drew; Salvador, John; Eismann, Michael T.; Barnard, Kenneth J.; Kershenstein, John

doi:10.1117/12.604471

Cited by 27 publications

(10 citation statements)

References 4 publications

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“…The simplest way to employ the above anomaly detection algorithm in a realtime application is to process the continuously recorded data in blocks, similar to what is done in the ARCHER system [19]. Each newly recorded block may thus be sent off to processing, provided that processing of the previous block is finished.…”

Section: Anomaly Detection Algorithmmentioning

confidence: 99%

“…Several real-time anomaly detection methods suitable for on-board processing exist, like the SSRX implemented in the ARCHER and WAR HORSE programs [18,19], but these are usually based on very simple geometric or statistical representations of the image background variability. In contrast, mixture models, such as the multivariate normal mixture model, may be able to represent the background variability quite accurately, resulting in statistically meaningful background metrics.…”

Section: Introductionmentioning

confidence: 99%

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Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Tarabalka

Haavardsholm

Kåsen

et al. 2008

J Real-Time Image Proc

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Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multicore central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable realtime anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.

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Section: Anomaly Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Tarabalka

Haavardsholm

Kåsen

et al. 2008

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…The machine learning and data mining communities also contributed to the advances. For example, as the computing resources to process hyperspectral images are becoming more readily accessible to the science and engineering communities, new machine learning techniques and algorithms have been used to tackle more complex problems such as real-time detection of complex materials or targets that were impossible in the past (Stevenson et al 2005;Stellman et al 2000;Yoon et al 2011;Tarabalka et al 2009;Heras 2011).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Processing Methods

Yoon

Park

2015

Food Engineering Series

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“…Detection methods that exploit color imagery of the search area provide one way for locating lost persons [1], but are limited in capability. Hyperspectral data provides a diverse set of features that can possibly improve the successes in SAR, and are currently being exploited by the Civil Air Patrol (CAP) Airborne Real-Time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) system [2]. Data cubes collected by airborne hyperspectral cameras can be analyzed to determine if there is something in the image worthy of further investigation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Human Skin in Near Infrared Hyperspectral Imagery

Nunez¹,

Mendenhall²

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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One of the difficulties in search and rescue missions is finding a small target, such as a person, in a large cluttered area. Airborne hyperspectral cameras are now being deployed to aid in this SAR mission. Motivated by the successes of such systems, we define a hyperspectral model of human skin in the visible and near infrared regions of the spectra so we can exploit knowledge gained during the modeling process to aid in human skin detection. Based on observations of the skin model results, an efficient and robust skin detection algorithm using channels in the near infrared region of the spectra is developed. Our algorithm is denoted the Normalized Difference Skin Index, motivated by the Normalized Difference Vegetation Index used in the literature for detecting vegetation in hyperspectral imagery. We demonstrate the capabilities of our skin detection methodology to detect skin amongst objects known to cause false detections for methodologies using three channel color data.Index Terms-hyperspectral imagery, skin detection, skin modeling, search and rescue

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Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system

Cited by 27 publications

References 4 publications

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Hyperspectral Image Processing Methods

Detection of Human Skin in Near Infrared Hyperspectral Imagery

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