Title

A hierarchical classifier for multispectral satellite imagery

Document Type

Journal Article

Publisher

Institute of Electronics, Information and Communication, Engineers, IEICE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

803

Comments

Originally published as: Bouzerdoum, A. (2001). A hierarchical classifier for multispectral satellite imagery. IEICE transactions on electronics, 84(12), 1952-1958. Original article available here

Abstract

In this article, a hierarchical classifier is proposed for classification of ground-cover types of a satellite image of Kangaroo Island, South Australia. The image contains seven ground-cover types, which are categorized into three groups using principal component analysis. The first group contains clouds only, the second consists of sea and cloud shadow over land, and the third contains land and three types of forest. The sea and shadow over land classes are classified with 99% accuracy using a network of threshold logic units. The land and forest classes are classified by multilayer perceptrons (MLPs) using texture features and intensity values. The average performance achieved by six trained MLPs is 91%. In order to improve the classification accuracy even further, the outputs of the six MLPs were combined using several committee machines. All committee machines achieved significant improvement in performance over the multilayer perceptron classifiers, with the best machine achieving over 92% correct classification.

 
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