An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2005

AUTHORS

Luca Pugliese , Silvia Scarpetta , Anna Esposito , Maria Marinaro

ABSTRACT

In this paper we test the performance of two unsupervised clustering strategies for the analysis of LANDSAT multispectral images of the Temples of Paestum Area in Italy. The classification goal is to identify environmental factors (soils, vegetation types, water) on the images, exploiting the features of the seven LANDSAT spectral bands. The first strategy is a fast migrating means technique based on a Maximum Likelihood Principle (ISOCLUST algorithm),and the second is the Kohonen Self Organizing Map (SOM) neural network. The advantage of using the SOM algorithm is that both the information on classes and the similarity between the classes are obtained (since proximity corresponds to similarity among neurons). By exploiting the information on class similarity it was possible to automatically colour each cluster identified by the net (assigning a specific colour to each of them) thus facilitating a successive photo-interpretation. More... »

PAGES

1190-1197

Book

TITLE

Image Analysis and Processing – ICIAP 2005

ISBN

978-3-540-28869-5
978-3-540-31866-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11553595_146

DOI

http://dx.doi.org/10.1007/11553595_146

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040086763


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