Classification of multispectral images using hierarchical random fields

Hajime Futatsugi; Sadao Fujimura

doi:10.1117/12.331861

4 December 1998 Classification of multispectral images using hierarchical random fields

Hajime Futatsugi, Sadao Fujimura

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331861
Event: Remote Sensing, 1998, Barcelona, Spain

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.

Citation Download Citation

Hajime Futatsugi and Sadao Fujimura "Classification of multispectral images using hierarchical random fields", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331861

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