The effect of spatial resolution upon texture‐based cloud field classifications

Abstract

The previous cloud classification investigations in parts 1–3 of this study have been conducted using 1/16‐km Landsat Multispectral Scanner (MSS) imagery. However, for global monitoring, much lower spatial resolutions of the order of 1–8 km generally are used. The present study examines the loss of cloud classification accuracy as a function of spatial resolution by degrading the imagery through progressive averaging. Textural measures are computed using the Gray Level Difference Vector approach. Significant improvement in cloud classification accuracy can be obtained using 1/2‐km spatial resolution data rather than the current 1‐km resolution data available today from AVHRR and GOES. Cirrus classification accuracy is especially compromised as the spatial resolution is degraded. However, the use of texture measures defined at the combination of pixel separations d = 1, 4 improves classification accuracies by several percent even for 1‐km spatial resolution data. Cirrus accuracy is significantly improved by use of multiple distance features. Classification accuracies using 1/8‐km spatial resolution data are similar to those obtained using the full spatial resolution features. The implications are that there are no advantages to be gained in cloud classification accuracies by using even higher spatial resolutions available from Landsat Thematic Mapper or SPOT imagery. Finally, it is found that multiple resolution imagery can be used to improve classification accuracy. Indeed, the “global” rather than the “local” aspects of texture appear to be most important to the classifier.