Pattern recognition of clouds and ice in polar regions

It is widely recognized that cloud classification schemes based upon multispectral signatures and clustering measures are severely limited over snow- and icecovered surfaces. This is due to the similarity of cloud and snow/ice spectral signatures in both visible and infrared wavelengths. Infrared threshold techniques are limited in particular by persistent surface inversions and warm lowlevel clouds. However pattern recognition schemes based upon the combination of spectral and textural signatures can be used effectively for cloud discrimination over high albedo surfaces. This study is based primarily upon AVHRR LAC imagery but with some results from LANDSAT high spatial resolution scenes. A large number of textural features are investigated including the Gray Level Difference Vector (GLDV) and Sum and Difference Histogram (SADH) approaches various features proposed by Garand the Gray Level Run Length (GLRL) spatial coherence " footprints and spectral histogram measures. Twenty arctic surface and cloud classes are identified using two different classificatiofl approaches: 1) the traditional stepwise discrirninant analysis and 2) neural network analysis. Principal component analysis of textural measures is used to eliminate those measures which contribute little to class separability. The neural network feed-forward back-propagation approach produces the highest classification accuracy and does so with a relatively small training set. However the main limitation is the long training times required. A new hybrid architecture using a modularized competitive learning layer inserted before the feed-forward backpropagation layer developed by Lee