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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
As table 3 shown, the correlation of spectral curve among the
centre pixel and each pixel in texture unit has obvious
difference especially for the resident texture unit. And different
object has very similar correlation which will lead the pixel
confuse for the pixel classification. The fractal dimension value
can obtain better result for the classification. The different
object has different fractal dimension value of spectral curve in
texture unit. The resident object has the fractal dimension value
ranged with 1.0199 ~ 1.0271, the tree ranged with 1.0152 ~
1.0210 and water ranged with 1.0105 ~ 1.076. Thus the
dimensionality reduction result using the fractal feature of
spectral curve can realize better texture code matching which is
very useful for image classification.
For the Huges phenomenon, there are still some of different
object fractal dimension feature while the fractal dimension of
the centre pixel is quite difference. The dimensionality
reduction based on spectral curve fractal analysis can combine
the spectral information and spatial texture information together
to realize the feature analysis and it can obtain better processing
efficiency of hyper spectral data. The dimensionality reduction
based on spectral curve fractal analysis provide a new method
to differ the confuse pixel such as the feature analysis combined
the spatial fractal analysis and spectral fractal.
ACKNOWLEDGEMENTS
The research work is supported by the Nation Science Founder
of China. (NSFC: 40371079) and 973 Project of China
(2006CB701303).
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