Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

Universal and language-specific patterns of categorisation

Previous work on object classification preferences shows that speakers of languages that lack morphological plural marking (like Yucatec and Japanese) display a tendency to match objects by common material, while speakers of languages with morphological plural marking (like English) display a tendency to match objects by common shape. The present paper compares categorisation preferences of English and Japanese speakers with those of Greek speakers. Greek resembles English in that it has morphological plural marking, but contrasts with English in that mass nouns typically do not resist pluralization. Results show that all groups distinguish significantly between countable objects and non-countable substances, but the degree to which they do this differs and conforms to language-specific grammatical patterns. It is argued that the effects of grammatical structure on categorisation preferences are finer-grained than earlier studies have assumed, thus providing a more precise account of the extent and nature of linguistic influence on cognition.