Recent developments in remote sensing technologies have made high resolution remotely sensed data such as hyperspectral and synthetic aperture radar (SAR) data readily available to detect and classify objects on the earth using pattern recognition. However, the dimensionality of such remotely sensed data is often large relative to the number of training samples available. Hence, dimensionality reduction technologies are often adopted to overcome the “curse of dimensionality” phenomenon. This present thesis focuses on the problem of dimensionality reduction of remote sensing data by proposing two algorithms for robust classification of hyperspectral and SAR data. Specifically, for hyperspectral image analysis, a genetic algorithm based feature selection and linear discriminant analysis based dimensionality reduction method is proposed, and, for SAR data, polarization channel based feature grouping followed by a multi-classifier, decision fusion technique is proposed. The algorithmic framework of the proposed approaches and experimental results will be presented in this thesis.

A measure of the quality of Voronoi tessellations resulting from various mesh generators founded on feature-driven models is introduced in this work. A planar tessellation covers an image with polygons of various shapes and sizes. Tessellations have potential utility due to their geometry and the opportunity to derive useful information from them for object recognition, image processing and classification. Problem domains including images are generally feature-endowed, non-random domains. Generators modeled otherwise may easily guarantee quality of meshes but certainly bear no reference to features of the meshed problem domain. They are therefore unsuitable in point pattern identification, characterization and subsequently the study of meshed regions. We therefore found generators on features of the problem domain. This provides a basis for element quality studies and improvement based on quality criteria. The resulting polygonal meshes tessellating an n-dimensional digital image into convex regions are of varying element qualities. Given several types of mesh generating sets, a measure of overall solution quality is introduced to determine their effectiveness. Given a tessellation of general and mixed shapes, this presents a challenge in quality improvement. The Centroidal Voronoi Tessellation (CVT) technique is developed for quality improvement and guarantees of mixed, general-shaped elements and to preserve the validity of the tessellations. Mesh quality indicators and entropies introduced are useful for pattern studies, analysis, recognition and assessing information. Computed features of tessellated spaces are explored for image information content assessment and cell processing to expose detail using information theoretic methods. Tessellated spaces also furnish information on pattern structure and organization through their quality distributions. Mathematical and theoretical results obtained from these spaces help in understanding Voronoi diagrams as well as for their successful applications. Voronoi diagrams expose neighbourhood relations between pattern units. Given this realization, the foundation of near sets is developed for further applications. / February 2017