Measurement of polar cloud cover is important because of its strong radiative influence on the energy balance of the snow and ice surface. Conventional satellite cloud detection schemes often fail in the polar regions because the visible and thermal contrasts between cloud and surface are typically small. Nevertheless, experts looking at satellite imagery can distinguish clouds from the surface by examining the textural characteristics of the scene. This paper describes an automated pattern recognition algorithm winch identities regions of various surface and cloud types at high latitudes from visible, near-infrared, and infrared AVHRR satellite data. Five spectral features give information about the magnitude of albedos and brightness temperatures, while three textural features describe the variability and “bumpiness” in a scene. The maximum likelihood decision rule is used to classify that region into one of seven surface categories or 11 cloud categories. The algorithm was able to classify 870... Abstract Measurement of polar cloud cover is important because of its strong radiative influence on the energy balance of the snow and ice surface. Conventional satellite cloud detection schemes often fail in the polar regions because the visible and thermal contrasts between cloud and surface are typically small. Nevertheless, experts looking at satellite imagery can distinguish clouds from the surface by examining the textural characteristics of the scene. This paper describes an automated pattern recognition algorithm winch identities regions of various surface and cloud types at high latitudes from visible, near-infrared, and infrared AVHRR satellite data. Five spectral features give information about the magnitude of albedos and brightness temperatures, while three textural features describe the variability and “bumpiness” in a scene. The maximum likelihood decision rule is used to classify that region into one of seven surface categories or 11 cloud categories. The algorithm was able to classify 870...