Evolution and Evaluation of a Trainable Cloud Classifier
Ari Visa and Jukka Iivarinen
Helsinki University of Technology
Laboratory of Information and Computer Science
Rakentajanaukio 2 C, FIN-02150 Espoo
Finland
Abstract
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 well.
IEEE Transactions on Geoscience and Remote Sensing, volume 35, no. 5, pp. 1307-1315, September 1997.
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