Amanda Ziemann

TL;DR: There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks, and a strategy for coupling climate and epidemiological models for zoonotic infectious diseases is provided.

TL;DR: Results show that the un Mixing residual error of each pixel's spectrum from real data, estimated from the spatially adaptive methodology, is reduced when compared to a global scale EM estimation and linear unmixing methodology.

TL;DR: In this article, the spectral response for a given material exhibits considerable variability from a variety of causes: intrinsic, depending on the composition or morphology of the material, extrinsic (depending on the size of an object or the concentration of the materials), or environmental (due to illumination, atmospheric distortion, and so on).

TL;DR: The Simplex Volume Estimation algorithm (SVE), which avoids potential hindrances by taking a geometrical approach to approximate the convex hull enclosing the data through identification of the simplex vertices (known as endmembers).

TL;DR: The concept of the linear mixture model is applied to the question of spectral image complexity at spatially local scales and the ultimate application here is large area image search without a priori information regarding the target signature.