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Characterizing Hyperspectral Imagery (AVIRIS) Using Fractal TechniqueWith the rapid increase in hyperspectral data acquired by various experimental hyperspectral imaging sensors, it is necessary to develop efficient and innovative tools to handle and analyze these data. The objective of this study is to seek effective spatial analytical tools for summarizing the spatial patterns of hyperspectral imaging data. In this paper, we (1) examine how fractal dimension D changes across spectral bands of hyperspectral imaging data and (2) determine the relationships between fractal dimension and image content. It has been documented that fractal dimension changes across spectral bands for the Landsat-TM data and its value [(D)] is largely a function of the complexity of the landscape under study. The newly available hyperspectral imaging data such as that from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) which has 224 bands, covers a wider spectral range with a much finer spectral resolution. Our preliminary result shows that fractal dimension values of AVIRIS scenes from the Santa Monica Mountains in California vary between 2.25 and 2.99. However, high fractal dimension values (D > 2.8) are found only from spectral bands with high noise level and bands with good image quality have a fairly stable dimension value (D = 2.5 - 2.6). This suggests that D can also be used as a summary statistics to represent the image quality or content of spectral bands.
Document ID
19990079432
Acquisition Source
Marshall Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Qiu, Hong-Lie
(California State Univ. Los Angeles, CA United States)
Lam, Nina Siu-Ngan
(Louisiana State Univ. Baton Rouge, LA United States)
Quattrochi, Dale
(NASA Marshall Space Flight Center Huntsville, AL United States)
Date Acquired
August 19, 2013
Publication Date
January 1, 1997
Subject Category
Earth Resources And Remote Sensing
Distribution Limits
Public
Copyright
Other

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