K.S. Chen

TL;DR: The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions.

TL;DR: This method efficiently reduces the data volume while retaining highly acceptable classification accuracy and the overall accuracy can be as high as 95% with a total of thirteen cover types classified.

TL;DR: A parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements is developed.

TL;DR: The complementary field coefficients are rederived based on a new surface slope expressions which are dependent on spatial variables which leads to a more complete expression of the multiple scattering terms, thus allowing the authors to account for multiple effects more accurately.

TL;DR: A fuzzy version of a dynamic learning neural network (DL) based on two steps: network representation of fuzzy logic and assignment of membership and Experimental results show that the FDL has faster convergence rate than that of DL and the separability between similar classes is improved.