Abstract: Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a… Read More
Month: July 2018
Congratulations Mr. Shashank Avusali, our lab’s most recent M.S. graduate!
Congratulations to Mr. Shashank Avusali for graduating with his M.S. this past week! His thesis is titled “Three Dimensional Reconstruction of Plant Roots via Low Energy X-ray Computed Tomography.” His research focused on generating a three-dimensional model of a plant… Read More
A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems
Abstract: Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is… Read More
Welcome new PhD student Matthew Cook!
The Machine Learning and Sensing Lab is excited to welcome Matthew Cook to our lab as a new Ph.D. student! Matthew has recently been awarded the Graduate School Preeminence Award to fund his studies at the University of Florida! He… Read More