Unsupervised Segmentation of Multispectral Images with Cellular Automata

TL;DR

This paper introduces a novel unsupervised classification method for multispectral satellite images using deterministic cellular automata, leveraging seed-based initialization and topological information for improved segmentation.

Contribution

It presents a new cellular automaton-based approach for unsupervised multispectral image segmentation, emphasizing seed selection and topological analysis.

Findings

Effective segmentation of multispectral images demonstrated

Automaton approach considers spatial and spectral diversity

Method allows flexible scale adjustment

Abstract

Multispectral images acquired by satellites are used to study phenomena on the Earth's surface. Unsupervised classification techniques analyze multispectral image content without considering prior knowledge of the observed terrain; this is done using techniques which group pixels that have similar statistics of digital level distribution in the various image channels. In this paper, we propose a methodology for unsupervised classification based on a deterministic cellular automaton. The automaton is initialized in an unsupervised manner by setting seed cells, selected according to two criteria: to be representative of the spatial distribution of the dominant elements in the image, and to take into account the diversity of spectral signatures in the image. The automaton's evolution is based on an attack rule that is applied simultaneously to all its cells. Among the noteworthy advantages of deterministic cellular automata for multispectral processing of satellite imagery is the consideration of topological information in the image via seed positioning, and the ability to modify the scale of the study.