All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 278 91 10
PDF Downloads 149 59 9

Cloud Classification of AVHRR Imagery in Maritime Regions Using a Probabilistic Neural Network

View More View Less
  • 1 Naval Research Laboratory, Monterey, California
Full access

Abstract

Using Advanced Very High Resolution Radiometer data, 16 pixel × 16 pixel sample areas are classified into one of ten output classes using a probabilistic neural network (PNN). The ten classes are cirrus, cirrocumulus, cirrostratus, altostratus, nimbostratus, stratocumulus, stratus, cumulus, cumulonimbus, and clear. Over 200 features drawn from spectral, textural, and physical measures are computed from the pixel data for each sample area. The input patterns presented to the neural network are a subset of these features selected by a routine that indicates the discriminatory potential of each feature.

The training and testing input data used by the PNN are obtained from 95 expertly labeled images taken from seven maritime regions; these images provide 1633 sample areas. Theoretical accuracy of the PNN classifier is determined using two methods. In the hold-one-cut method, the network is trained on all data samples minus one and is tested on the, remaining sample. Using this technique, 79.8% of the samples are classified correctly. A bootstrap method of 100 randomly determined sample sets produces an average overall accuracy of 77.1%, with a standard deviation of 1.4%. In a more general classification using five classes (low clouds, altostratus, high clouds, precipitating clouds, and clear), 91.2% of the samples are accurately classified. A two-layer, four-network system that determines the general classification of a sample followed by a specific classification in another network is proposed. Testing of this system produces mixed results compared to the single ten-class PNN.

Abstract

Using Advanced Very High Resolution Radiometer data, 16 pixel × 16 pixel sample areas are classified into one of ten output classes using a probabilistic neural network (PNN). The ten classes are cirrus, cirrocumulus, cirrostratus, altostratus, nimbostratus, stratocumulus, stratus, cumulus, cumulonimbus, and clear. Over 200 features drawn from spectral, textural, and physical measures are computed from the pixel data for each sample area. The input patterns presented to the neural network are a subset of these features selected by a routine that indicates the discriminatory potential of each feature.

The training and testing input data used by the PNN are obtained from 95 expertly labeled images taken from seven maritime regions; these images provide 1633 sample areas. Theoretical accuracy of the PNN classifier is determined using two methods. In the hold-one-cut method, the network is trained on all data samples minus one and is tested on the, remaining sample. Using this technique, 79.8% of the samples are classified correctly. A bootstrap method of 100 randomly determined sample sets produces an average overall accuracy of 77.1%, with a standard deviation of 1.4%. In a more general classification using five classes (low clouds, altostratus, high clouds, precipitating clouds, and clear), 91.2% of the samples are accurately classified. A two-layer, four-network system that determines the general classification of a sample followed by a specific classification in another network is proposed. Testing of this system produces mixed results compared to the single ten-class PNN.

Save