Share Email Print

Proceedings Paper

Ecosystem classification using artificial intelligence neural networks and very high spatial resolution satellite imagery
Author(s): Iphigenia Keramitsoglou; Haralambos Sarimveis; Chris T. Kiranoudis; Nicolaos Sifakis
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This study investigates the potential of classifying complex ecosystems by applying the radial basis function (RBF) neural network architecture, with an innovative training method, on multispectral very high spatial resolution satellite images. The performance of the classifier has been tested with different input parameters, window sizes and neural network complexities. The maximum accuracy achieved by the proposed classifier was 78%, outperforming maximum likelihood classification by 17%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. The new technique was applied to the area of Lake Kerkini (Greece), a wetland of great ecological value, included in the NATURA 2000 list of ecosystems.

Paper Details

Date Published: 24 February 2004
PDF: 9 pages
Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); doi: 10.1117/12.511041
Show Author Affiliations
Iphigenia Keramitsoglou, National Observatory of Athens (Greece)
Haralambos Sarimveis, National Technical Univ. of Athens (Greece)
Chris T. Kiranoudis, National Technical Univ. of Athens (Greece)
Nicolaos Sifakis, National Observatory of Athens (Greece)

Published in SPIE Proceedings Vol. 5232:
Remote Sensing for Agriculture, Ecosystems, and Hydrology V
Manfred Owe; Guido D'Urso; Jose F. Moreno; Alfonso Calera, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?