Mixture Regression for Sea Ice Segmentation

The classification of sea ice in SAR imagery is complicated by statistical nonstationarity. Incidence angle effects, heterogeneous ice conditions and other confounding variables contribute to spatial and temporal variability in the appearance of sea ice. I explore a family of models called mixture regressions which address this issue by endowing mixture distributions with class-dependent trends. I introduce mixture regression as a general technique for unsupervised clustering on nonstationary datasets and propose techniques to improve its robustness in the presence of noise and outliers. I then develop region-based mixture regression models for sea ice segmentation, focusing on the modeling of SAR backscatter intensities under the influence of incidence angle effects. Experiments are conducted on various extensions to the approach including the use of robust estimation to improve model convergence, the incorporation of Markov random fields for contextual smoothing, and the combination of mixture regression with supervised classifiers. Performance is evaluated for ice-water classification on a set of dual-polarized RADARSAT-2 images taken over the Beaufort Sea. Results show that mixture regression achieves accuracy of 92.8% in the unsupervised setting and 97.5% when integrated with a supervised convolutional neural network. This work improves on existing techniques for sea ice segmentation which enable operational ice mapping and environmental monitoring applications. The presented techniques may also be useful for the segmentation of nonstationary images obtained from other remote sensing techniques or in other domains such as medical imaging

Optimal control using sparse-matrix belief propagation

Treball fi de màster de: Master in Intelligent Interactive SystemsTutor: Vicenç Gómez CerdàThe optimal control framework is a mathematical formulation by means of which many decision making problems can be represented and solved by finding optimal policies or controls. We consider the class of optimal control problems that can be formulated as a probabilistic inference on a graphical model, known as Kullback- Leibler (KL) control problems. In particular, we look at the recent progress on exploiting parallelisation facilitated by the graphics processing units (GPU) to solve such inference tasks, considering the recently introduced sparse-matrix belief propagation framework [1]. The sparse-matrix belief propagation algorithm was reported to deliver significant improvements in performance with respect to traditional loopy belief propagation, when tested on grid Markov random fields. We develop our approach in the context of the KL-stag hunt game, a multi-agent, grid-like game which shows two different behavior regimes [2]. We first describe how to transform the original problem into a pairwise Markov random field, amenable to inference using sparse-matrix belief propagation and, second, we perform an experimental evaluation. Our results show that the use of GPUs can bring notable performance improvements to the optimal control computations in the class of KL control problems. However, our results also suggest that the improvements of sparse-matrix belief propagation may be limited by the concrete form of the Markov random field factors, specially on models with high sparsity within a factor, and variables with high cardinality