Evolution and evaluation of a trainable cloud classifier

Neural network classifiers have recently been popular in image classification and remote sensing applications. In this paper a case study is reported, where the evolution started with a pure neural network based solution and reached a simplified classifier with a few neural network properties. This seems to be a typical evolution concerning neural networks. A multispectral cloud classifier was implemented to automate the interpretation of AVHRR (Advanced Very High Resolution Radiometer) images. It can be adapted to changing situations with new examples. This is a requirement in satellite image applications, hence changes in illumination, round the year, during day and night, and aging of electronics are possible. The classification is done in two phases, clouds are separated from the background and then only clouds are classified. The evaluation of the classifier is based on the comparison between the SYNOP observations and the satellite observations. Comparisons with other published results show that the classifier is working