Unsupervised Segmentation of Multispectral Images with Cellular Automata
This work addresses the problem of analyzing Earth surface phenomena from satellite data for researchers in remote sensing, but it appears incremental as it builds on existing unsupervised techniques with a specific algorithmic approach.
The paper tackles unsupervised classification of multispectral satellite images by proposing a deterministic cellular automaton method, which groups pixels based on spatial and spectral criteria without prior terrain knowledge.
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.