Cloud field classification based upon high spatial resolution textural features: 1. Gray level co‐occurrence matrix approach

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Abstract
Standard cloud algorithms rely on multispectral signatures to identify high, medium, and low clouds. In contrast, the present study classifies stratocumulus, cumulus, and cirrus clouds, using textural features alone, derived from a single high‐resolution Landsat Multispectral Scanner near‐infrared channel. Applying stepwise linear discriminant analysis, classification accuracies of 92–95%, with standard deviations of about 1.5%, are achieved using the random subregion hold‐out pattern. Accuracies of 83–88%, with standard deviations of about 7%, are achieved using the random scene hold‐out pattern. The theoretical accuracy of the approach is estimated by treating the simulation on a non‐parametric basis, using the bootstrap method. It is significant that the present method is capable of distinguishing high cirrus clouds from low clouds strictly on the basis of spatial brightness patterns. In fact, cirrus has the highest classification accuracy with this approach. The largest probability of misclassification is associated with confusion between stratocumulus breakup regions and fair‐weather cumulus. The present study is based upon textural features computed from Gray Level Co‐occurrence Matrix (GLCM) statistics defined at various pixel displacement distances. It is found that textural features defined at pixel separations of 0.5 km produce classification accuracies equal to or better than those defined at separations of 57 m. Some slight improvement (2–3%) in classification accuracy is achieved by inclusion of textural features defined at multiple pixel separations. In general, stratocumulus classification is improved by inclusion of additional features defined at pixel separations d < 0.25 km, cirrus by features at d ≃ 1 km, and cumulus by features at d ≳ 2 km.

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