Tasseled Tap Transformation and Neural Networks for the Design of an Optimum Image Classification Algorithm using Multispectral Data

The paper aims at improving image automatic classification from remote sensing data using neural network algorithms (NN). NN have been found to have good generalization properties and their use is becoming increasingly prevalent in the field of remote sensing and in particular for image classification. However, the type of input to be considered for the algorithm in order to maximize the information available from the measurement is still an open issue. Using the mere spectral signature with no pre-processing is not an effective choice. Another point regards the use of textural features to improve the classification which involves taking decisions on how many and what specific features should be considered. More in general, minimizing the number of inputs of a neural network algorithm, avoiding significant loss of information, affects positively the NN mapping ability and computational efficiency. In this paper we propose a new methodology facing with the aforementioned problems and providing a solution to them.