Three-dimensional extension of the unit-feature spatial classification method for cloud type

Abstract

We describe how the Unit-Feature Spatial Classification Method (UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently. By using a combination of Interactive Data Language (IDL) and Visual C++ (VC) code in combination to extend the technique in three dimensions (3-D), this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification, so as to deal with the bi-spectral limitations of traditional two dimensional (2-D) UFSCM. The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery, resulting in more reasonable results and an improvement over the 2-D method.