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Proceedings Paper

Classification of multispectral images using hierarchical random fields
Author(s): Hajime Futatsugi; Sadao Fujimura
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Paper Abstract

In order to improve the correct classification rate of the conventional maximum likelihood method for classification of multi-spectral images, we introduce 'a priori probability' estimated from the spatial structure of the images. In this we consider the observed data as random field defined on a 2D lattice. Each pixel has a class label which is also regarded as random field on the lattice. Then the spatial structure of an image is expressed by the dependence of a label on its neighbors. We use local and global spatial information of an image in classification process by making a point in the label lattice have both local and global interrelations. To accomplish this, we use pyramidal (hierarchical) 3D lattice. A priori probability is determined by transition probability from one layer of the lattice to another. It was confirmed that our method improved correct classification rate by about 20% compared with that obtained by the conventional maximum likelihood method or co-occurrence probability method.

Paper Details

Date Published: 4 December 1998
PDF: 8 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331861
Show Author Affiliations
Hajime Futatsugi, Univ. of Tokyo (Japan)
Sadao Fujimura, Univ. of Tokyo (Japan)


Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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