Polarimetric Synthetic Aperture Radar (POLSAR) data is an important source for many operational remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. In this thesis, spectral graph partitioning methodology is used to exploit both the polarimetric attributes of pixels, and the visual aspect of the image data through visual cues.

The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process.

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Lecture Notes on multiview point cloud regularization (MVPCR), a framework that unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces. Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL.