Classification of hyperspectral data using spectral-spatial approaches

TL;DR: This thesis proposes and develops novel spectral-spatial methods and algorithms for accurate classification of hyperspectral data and explores possibilities of high-performance parallel computing on commodity processors for reducing computational loads.

Abstract: Hyperspectral imaging records a detailed spectrum of the received light in each spatial position in the image. Since different substances exhibit different spectral signatures, hyperspectral imagery is a well-suited technology for accurate image classification, which is an important task in many application domains. However, the high dimensionality of the data presents challenges for image analysis. While most of the previously proposed classification techniques process each pixel independently without considering information about spatial structures, recent research in image processing has highlighted the importance of the incorporation of spatial context in a classifier. In this thesis, we propose and develop novel spectral-spatial methods and algorithms for accurate classification of hyperspectral data. First, the integration of the Support Vector Machines (SVM) technique within a Markov Random Fields (MRFs) framework for context classification is investigated. SVM and MRF models are two powerful tools for high-dimensional data classification and for contextual image analysis, respectively. In a second step, we propose classification methods using adaptive spatial neighborhoods derived from region segmentation results. Different segmentation techniques are investigated and extended to the case of hyperspectral images. Then, approaches for combining the extracted spatial regions with spectral information in a classifier are developed. In a third step, we concentrate on approaches to reduce oversegmentation in an image, which is achieved by automatically “marking” the spatial structures of interest before performing a marker-controlled segmentation. Our proposal is to analyze probabilistic classification results for selecting the most reliably classified pixels as markers of spatial regions. Several marker selection methods are proposed, using either individual classifiers, or a multiple classifier system. Then, different approaches for marker-controlled region growing are developed, using either watershed or Minimum Spanning Forest methods and resulting in both segmentation and context classification maps. Finally, we explore possibilities of high-performance parallel computing on commodity processors for reducing computational loads. The new techniques, developed in this thesis, improve classification results, when compared to previously proposed methods, and thus show great potential for various image analysis scenarios.