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Analytical framework for modeling scale-related variabilities in remote sensing

A general analytical framework was established to investigate the scale-related variabilities in remote
sensing. The variabilities were studied first by investigating canopy structure, canopy interaction with
light, relation between spectral reflectance and plant phenological parameters. The variabilities
simulated by the plant model were compared with the actual spectral data acquired by ground
spectroradiometer and satellite sensors. The theoretical relation between orthogonal-basedtransform
and Kahunen-Lo6ve transform was investigated in the vector space. The role of spectral indices in
identifying the status of phenological parameters was briefly studied.
The radiometric corrections of the remotely sensed data were carefully controlled to avoid the
unwanted noise introduced by typical resampling/correction procedures from commercial operation.
The non-linearity and sensor response corrections were applied to the spectral data as necessary.
Variability analysis was conducted to illustrate the complexities of spectral variability embedded in
the remotely sensed data.
The information extraction in spatial frequency domain was investigated with emphasis in Fourier
domain feature extraction. The Radon transform was introduced as the potential tool to enhance the
spatial information of the Fourier transformed image. The adequacy of entropy and fractal dimension
as image information measures was proved. A functional link between entropy and fractal dimension
was established. The image information content was extracted using various first and second order
statistics, entropy, and fractal dimension. Results were presented for different remote sensors based
on the full image information content and specific agricultural ground features. The quality of spatial
resampling algorithms was tested by investigating the capability to maintain image information in the
resampled image. Finally, two applications utilizing this analytical framework were presented to show
its potential in land-use classification and multiscale data fusion. / Graduation date: 1993

Identiferoai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/36777
Date27 July 1992
CreatorsChen, Chaur-fong
ContributorsCuenca, Richard
Source SetsOregon State University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation

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