Cloud field classification based upon high spatial resolution textural features: 2. Simplified vector approaches

Abstract

The huge volume of data being collected in global climate studies makes it necessary to develop efficient automatic data analysis methods. While most cloud classification algorithms are based upon multispectral signatures, there is growing use of textural features. The results given in Part 1 of this study demonstrate that textural features computed from the Gray Level Cooccurrence Matrix (GLCM) approach produce high cloud classification accuracies. The present study compares classification results derived from two vector approaches, Sum and Difference Histogram (SADH) and Gray Level Difference Vector (GLDV), with those from the GLCM approach. It is found that the SADH approach produces accuracies equivalent to those obtained using GLCM, but with greater ability to resolve error clusters; also, there is a 30% savings in run time and a 50% savings in storage requirements. The GLDV approach suffers a slight degradation in classification accuracy but has a 40% savings in run time and an 87% savings in storage requirements. Textural features are not highly sensitive to moderate variations in cloud threshold selection. However, the whole cloud, rather than only the brightest portions of the cloud, produce the highest classification accuracies. A very important result is that spatial information content and classification accuracy are preserved even at lower radiometric resolutions with effective gray levels of 16. means that significantly low resolution digitized versions of satellite imagery retain essentially the full spatial information content of the original digital data. Substitution of digitized imagery can significantly reduce the expense of many remote sensing studies.