A neural network approach to cloud classification

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93%. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96%, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92%, cumulus at 90%. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. The present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared.>

# Remote-Sensing Image Classification # Engineering # Atmospheric aerosols and clouds # Environmental Science # Meteorological Phenomena and Simulations # Earth and Planetary Sciences

J. Lee, Ronald C. Weger, S.K. Sengupta, et al. (1990). "A neural network approach to cloud classification". IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.58972