Modeling covariance matrices in multitemporal temperature feature spaces

Multitemporal thermal imagery in conjunction with a diurnal temperature model provides a well-known means to derive physical properties of the Earth´s surface. Classification applications based on such data sets can be used for clustering image pixels with similar heating behaviour. For that purpose, the covariance matrix which describes the mutual dependence of the used features (i.e. temperature values measured at different times of a day) provides an important source of information. In this study we investigate the effect of Gaussian distributed surface properties (thermal inertia, humidity and albedo) on covariance matrices in multitemporal temperature feature spaces with the aid of a diurnal temperature model. The results are compared with experimental findings