Paper
31 October 1997 AVIRIS image feature separation and image data compression and reconstruction
James B. Farison, Bo Ma Cordell, Mark E. Shields
Author Affiliations +
Abstract
This paper presents results for the application of the orthogonal projection (OP) filter to AVIRIS hyperspectral images of the Lunar Crater Volcanic Field in Nevada. The OP filter is a special case of the simultaneous-diagonalization (SD) filter, developed to enhance a selected feature while suppressing other features and noise in an image scene. The SD filter applies to sets of images that are e spatially invariant (SI) and in which the individual features in the image scene and noise contribute linearly and additively (LA) to the recorded pixel image intensities. The OP filter uses the original LA SI image set and the signatures of the individual features to generate a new set of images in which the distinct features are separated. Applied to AVIRIS hyperspectral images, the OP filter performs spectral unmixing. The resulting filtered images are estimates of the spatial distributions, or endmembers, of the original image features, and can be used to reconstruct estimates of the original image. Since the number of individual features is much smaller than the number of images in the original set, this represents significant data compression. However, the filtered images are not perfect due to images are not perfect due to imaging system noise and imperfections in the fit of the LA SI model to the actual AVIRIS images. As a test of the accuracy of the compression method, this paper investigates the reconstruction of the original AVIRIS images from the small set of individual feature images.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James B. Farison, Bo Ma Cordell, and Mark E. Shields "AVIRIS image feature separation and image data compression and reconstruction", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283828
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Cited by 1 scholarly publication.
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KEYWORDS
Image filtering

Image compression

Feature extraction

Data compression

Image analysis

Hyperspectral imaging

Samarium

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