&lt;title&gt;Airborne visible hyperspectral imaging spectrometer: optical and system-level description&lt;/title&gt;

Meigs, Andrew D.; Butler, Eugene W.; Jones, Bernard Al; Otten, Leonard John; Sellar, R. Glenn; Rafert, Bruce

doi:10.1117/12.258074

Cited by 3 publications

(2 citation statements)

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“…We utilize the HIMP to provide physics-based data for simulation and analysis. 12 The HIMP is an interactive, spreadsheet-based computer model, which has modeled performance for several Fourier transform hyperspectral imagers, including the Kestrel VFTHSI 13 and MightySat II.1. 14,15 The HIMP includes parameters that allow the specification of numerous target, atmospheric, instrumental, geometrical, and detector characteristics, as well as a variety of graphical outputs.…”

Section: Hyperspectral Imager Model Programmentioning

confidence: 99%

Singular Spectrum Analysis: A Note on Data Processing for Fourier Transform Hyperspectral Imagers

et al. 2016

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Hyperspectral remote sensing is experiencing a dazzling proliferation of new sensors, platforms, systems, and applications with the introduction of novel, low-cost, low-weight sensors. Curiously, relatively little development is now occurring in the use of Fourier transform (FT) systems, which have the potential to operate at extremely high throughput without use of a slit or reductions in both spatial and spectral resolution that thin film based mosaic sensors introduce. This study introduces a new physics-based analytical framework called singular spectrum analysis (SSA) to process raw hyperspectral imagery collected with FT imagers that addresses some of the data processing issues associated with the use of the inverse FT. Synthetic interferogram data are analyzed using SSA, which adaptively decomposes the original synthetic interferogram into several independent components associated with the signal, photon and system noise, and the field illumination pattern.

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Section: Hyperspectral Imager Model Programmentioning

confidence: 99%

Singular Spectrum Analysis: A Note on Data Processing for Fourier Transform Hyperspectral Imagers

et al. 2016

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“…Each plot is normalized so that the maximum is 100 and the minimum is 0.Figure 3ashows a 10 x 10 region of the target band, yt. The pixels at(3,2),(8,2),(3,7), and (8,7)contain inserted targets. The relative target strengths are 70, 74, 100, and 72 respectively.…”

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confidence: 99%

<title>Spatial spectral feature extraction in hyperspectral imagery</title>

Winings

Fraser

1999

SPIE Proceedings

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In this paper, we present a practical and potential useful approach to spatial spectral feature extraction of hyperspectral imagery. Many new hyperspectral imaging sensors have collected hyperspectral data cubes in optical -infrared wavebands. The data cubes have two spatial dimensions and one spectral dimension providing hundreds of images of the same scene in different wavebands. Radiance clutter may interfere with the detection of specific target signals in the data cubes. But, target signals and background sources generally have different spatial features in different spectral bands. For example, target signals may have a higher contrast than the local background in one band but not in a different band. We exploit these band differences to detect the target signals and extract spatial and spectral features. In our analysis we use real image cube data from sensors such as the Fourier Transform Hyperspectral Imager (FTHSI). We combine traditional spatial processing, such as frame differencing and adaptive filters, but apply them to different image bands instead of different images of the same scene obtained at different times. We compute the probabilities of detection and false alarms for targets of a given strength against the measured optical clutter. We compare target detection algorithms using only one band with those using multiple bands.A proliferation of new hyperspectral image sensors has lead to new approaches to remote sensing and automatic target recognition 2, 3, 4, 5 These sensors have compiled archives of hyperspectral data at visible and infrared wavelengths. These data cubes possess unique properties that allow new methods of detecting desired target signatures in the presence of background clutter. Generally, the hyperspectral image cubes contains two spatial dimensions and many wavelength bands with a two-dimensional image for each band. Thus, if a spectral signature of target is known and it is different than the background clutter, detection algorithms that combine target spectrum with the target shape can enhance the probability of detection while decreasing the probability of false alarm.

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