Concurrent Self-Organizing Maps for Pattern Classification
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
A Neural Approach to Compression of Hyperspectral Remote Sensing Imagery
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
| Hi-index | 0.00 |
We investigate multispectral satellite image classification using the neural model previously proposed by the first author called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of self-organizing neural network modules. For comparison, we evaluate the performances of several statistical classifiers (Bayes, 1-NN, and K-means). The implemented neural versus statistical classifiers are evaluated using a LANDSAT 7 ETM+ image. One takes in considerations both the interband and intraband pixel correlation using a 63-dimensional representation of the 7-band pixels. There is a subset containing labeled pixels, corresponding to seven thematic categories. The best experimental result leads to the recognition rate of 99.11%.