Spatial spectral feature extraction in hyperspectral imagery

Michael J. Winings; James C. Fraser

doi:10.1117/12.353027

16 July 1999 Spatial spectral feature extraction in hyperspectral imagery

Michael J. Winings, James C. Fraser

Proceedings Volume 3717, Algorithms for Multispectral and Hyperspectral Imagery V; (1999) https://doi.org/10.1117/12.353027
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

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. We combine traditional spatial processing, such as frame differencing and adaptive filters, but apply them to different image band 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.

Citation Download Citation

Michael J. Winings and James C. Fraser "Spatial spectral feature extraction in hyperspectral imagery", Proc. SPIE 3717, Algorithms for Multispectral and Hyperspectral Imagery V, (16 July 1999); https://doi.org/10.1117/12.353027

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KEYWORDS

Detection and tracking algorithms

Target detection

Hyperspectral imaging

Optimal filtering

Hyperspectral target detection

Composites

Image processing

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