Multispectral Image Classification Using Multilayer Perceptron and Principal Components Analysis

This work presents a methodology for pattern classification from multispectral images acquired by the HSS airborne sensor. In order to achieve this purpose, a conjunction of Artificial Neural Network and Principal Components Analysis has been used. The results indicate that this approach can be alternatively employed in multispectral images to separate materials with specific characteristics based on their reflectance properties.