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A combination of a symbolic and a natural classifier applied to Remote Sensing images case of study: Penang island

ACRS 1997

Digital Image Processing

A Combination of a symbolic and a natural classifier applied to
remote sensing images
Case of study: Penang Island

Lionel Beauge, Alain Ketterlin* Ahamad Tajudin Khader, Ruslan Rainis*, Jersy Korczak*
School of Computer Sciences and *School of Humanities
Universiti Sains Malaysia
11800 USM Pulau Pinang, Malaysia
Department d’Informatique de l’Universite & Louis Pasteur
67084 Strasbourg, France
E-mail : [email protected]


In remote sensing, images become more and more complex, stemming from the appearance of higher performance satellites. For years, several kinds of formal models have been applied to analyze them, but complex formalisms lead to high computational complexity e.g. morphology mathematical processings. The ongoing research focuses on a hybrid classifier which can discover automatically structured objects on images, in order to generate thematic maps. In this paper, two unsupervised models are presented. The models are based respectively on conceptual classification (the Cobweb algorithm) and on competitive neural network learning to validate our approach. The case of study is Penang Island which provides several kinds of landscape. The resulting maps are quite similar. Our results are also compared with the thematic map of an expert. Our research tend finally toward an hybrid model which integrates symbolic and neural learning, geometrical aspects and expert knowledge. Our main goal is to produce a map updating system whose maps can be used in the studies on environment monitoring e.g. land and urban utilization, vegetation distribution and its changes, regional development etc.

1. Introduction

Remote sensing images provide spectral values about the region under observation. These spectral data allow to distinguish different spectral forms in images by combining all the bands. But images are mote than a simple collection of spectral values. Pixels which are the basic units of the image are also spatially organized. In fact, spectral and spatial values are two kinds of sources of data which can be analysed in different phases or levels and can be combined to obtain the best classification. The goal of classification is to automatically create thematic maps of the region under observation.

2. Methods

2.1 Segmentation principle

Segmentation consists in finding regions or objects that present a thematic meaning regarding to the expert. It is closed to clustering by the fact that it analyses each pixel-element of the image to form a set of distinct classes. In remote sensing, clustering consists in finding a set of classes in the measurement space