Prasad, Saurabh2018-12-062018-12-06December 22016-12December 2http://hdl.handle.net/10657/3691Multi-source remote sensing data has the potential to enable robust image analysis. These sources can be images taken from different sensors on the same region of interest, or multiple different types of diverse features extracted from the same sensor. Either way, composite kernel methods can effectively use information from different sources for classification tasks. In recent work, Angular Discriminant Analysis (ADA) was developed as a technique for effective supervised dimensionality reduction of hyperspectral images. In this thesis, we present a composite-kernel variant of locality preserving ADA (CKLADA) for multi-source remote sensing image analysis. Experiments using a dataset that contains hyperspectral imagery (HSI), light detection and ranging (LiDAR) data and extended multi-attribute profile (EMAP) features, and a single-sensor dataset that contains HSI and EMAP features were conducted whose results show that proposed method provides very effective feature reduction for multi-source remotely sensed data.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Composite kernelAngular discriminant analysisMulti-source dataClassification2018-12-06Thesisborn digital