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Proceedings Paper

Application of spatial resolution enhancement and spectral mixture analysis to hyperspectral images
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Paper Abstract

Spatial resolution enhancement and spectral mixture analysis are two of the most extensively used image analysis algorithms. This paper presents an algorithm that merges the best aspects of these two techniques while trying to preserve the spectral and spatial radiometric integrity of the data. With spectral mixture analysis, the fraction of each material (endmember) in every pixel is determined from hyperspectral data. This paper describes an improved unmixing algorithm based upon stepwise regression. The result is a set of material maps where the intensity corresponds to the percentage of a particular endmember within the pixel. The maps are constructed at the spatial resolution of the hyperspectral sensor. The spatial resolution of the material maps is then enhanced using one or more higher spatial resolution images. Similar to the unmixing approach, different endmember contributions to the pixel digital counts are distinguished by the endmember reflectances in the sharpening band(s). After unmixing, the fraction maps are sharpened with a constrained optimization algorithm. This paper presents the results of an image fusion algorithm that combines spectral unmixing and spatial sharpening. Quantifiable results are obtained through the use of synthetically generated imagery. Without synthetic images, a large amount of ground truth would be required in order to measure the accuracy of the material maps. Multiple band sharpening is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. The analysis includes an examination of the effects of constraints and texture variation on the material maps. The results show stepwise unmixing is an improvement over traditional unmixing algorithms. The results also indicate sharpening improves the material maps. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map.

Paper Details

Date Published: 6 November 1996
PDF: 12 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257182
Show Author Affiliations
Harry N. Gross, Rochester Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2821:
Hyperspectral Remote Sensing and Applications
Sylvia S. Shen, Editor(s)

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