&lt;title&gt;The civil air patrol ARCHER hyperspectral sensor system&lt;/title&gt;

Stevenson, Brian; O'Connor, Rory; Kendall, William B.; Stocker, Alan D.; Schaff, William; Holasek, Rick E.; Even, Detlev; Alexa, Drew; Salvador, John; Eismann, Michael T.; Mack, Robert T.; Kee, Pat; Harris, Steve; Karch, Barry K.; Kershenstein, John

doi:10.1117/12.604458

Cited by 15 publications

(8 citation statements)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though the MDSI requires the acquisition of reflectance values at four different wavelengths and within a spectral range broader than the usual range (e.g., 380-1100 nm) covered by the most widely used detection systems, 2 we believe that it can effectively contribute to the reduction of search time, and thus increase the survival chances of those who are lost. Moreover, as pointed out by Eismann et al,…”

Section: Resultsmentioning

confidence: 99%

“…(2). However, it is important to note that there are materials and material combinations in nature (e.g., water and soil mixtures found in rivers, particularly during flooding situations) that are characterized by spectral profiles qualitatively similar to the spectral profiles of human skin in the 450-650 and 1450-1650 nm ranges.…”

Section: Definition and Effectiveness Assessmentmentioning

confidence: 99%

“…2 Despite recent advances in this area, however, the effective detection of human targets remains an open problem, notably in environments composed of background materials characterized by spectral features that pose limited contrast with skin spectral signatures. Such limited contrast may result in false positives, or false alarms, during timecritical search and rescue operations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multispectral index for the remote detection of human skin signatures

Baranoski

Chen

2015

Opt. Eng

View full text Add to dashboard Cite

Abstract. We propose a multispectral index to assist the detection of human signatures in complex natural environments. Differently from previously proposed indices, it takes into account the spectral responses of human skin not only in the near infrared, but also in the visible region of the light spectrum. As a result, it can contribute to mitigate the chances of false alarms during time-critical search and rescue operations carried out in such environments. Our investigation is supported by the use of reflectance data measured for different skin specimens and natural materials such as sand, ocean water, melting snow, and forest vegetation. We believe that the observations reported in this work can be incorporated into the design of more effective procedures and devices for the differentiation of human targets from background materials commonly found in nature.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Definition and Effectiveness Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multispectral index for the remote detection of human skin signatures

Baranoski

Chen

2015

Opt. Eng

View full text Add to dashboard Cite

show abstract

“…5. These are based on a single 238 × 208 image captured by the CAP ARCHER [62] airborne hyperspectral system over the National Test Pilot School in Mojave, CA. The synthetic change was created by swapping the indicated 8 × 8 pixel blocks in the test image.…”

Section: B Test Datamentioning

confidence: 99%

Extension of the Linear Chromodynamics Model for Spectral Change Detection in the Presence of Residual Spatial Misregistration

Vongsy

Eismann

Mendenhall

2015

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

A generalized likelihood ratio test (GLRT) statistic for spectral change detection based on the linear chromodynamics model is extended to accommodate unknown residual misregistration between imagery described by a prior probability density function for the spatial misregistration. Using a normal prior distribution leads to a fourth-order polynomial that can be numerically minimized over the unknown misregistration parameters. A more computationally efficient closed-form solution is developed based on a quadratic approximation and provides comparable results to the numerical minimization for the investigated test cases while running 30 times faster. The results applying the method to hyperspectral imagery indicate up to an order of magnitude reduction in false alarms at the same detection rate relative to baseline change detection methods for synthetically misregistered test data particularly in image regions containing edges and fine spatial features. Sensitivity to model parameters is assessed, and the method is compared with a previously published misregistration compensation approach yielding comparable results. Although the GLRT approach appears to exhibit comparable change detection performance, it offers the possibility of tailoring the algorithm to a priori knowledge of expected misregistration errors or to compensate structured misregistration as would occur due to parallax errors due to perspective variations (e.g., image parallax).Index Terms-Change detection, generalized likelihood ratio test (GLRT), hyperspectral, misregistration.

show abstract

“…1 were given in [18]. First, in order to address the void in suitable groundtruthed hyperspectral data to support quantitative spatialspectral target detection assessments, an experiment was designed and executed with the airborne real-time cueing hyperspectral enhanced reconnaissance (ARCHER) [19] sensor. We refer to the coincident HSI and HRI imagery from this collection as Bobcat 2013 [20].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Kaufman

Eismann

Celenk

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In this work, we assess the detection and classification of specially constructed targets in coincident airborne hyperspectral imagery (HSI) and high spatial resolution panchromatic imagery (HRI) in spectral, spatial, and joint spatial-spectral feature spaces. The target discrimination powers of the data-level and feature-level fusion of HSI and HRI are also directly compared in the spatial-spectral context using airborne imagery collected explicitly for this research. We show that in the case of Bobcat 2013 imagery, feature-level fusion of the HSI spectrum with spatial features derived from the coincident HRI data consistently results in fewer false alarms on the scene background as well as fewer misclassifications among the tested targets. Furthermore, this approach also outperforms schemes in which data-level fusion of the HSI and HRI imagery is performed prior to extracting spatial-spectral features.Index Terms-Hyperspectral imagery (HSI), image fusion, material identification, pansharpening, spatial-spectral feature extraction, target identification.

show abstract

<title>The civil air patrol ARCHER hyperspectral sensor system</title>

Cited by 15 publications

References 4 publications

Multispectral index for the remote detection of human skin signatures

Multispectral index for the remote detection of human skin signatures

Extension of the Linear Chromodynamics Model for Spectral Change Detection in the Presence of Residual Spatial Misregistration

Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Contact Info

Product

Resources

About