Earth observation and land-cover analysis became a reality in the last 2-3 decades thanks to NASA airborne and spacecrafts such as Landsat. Inclusion of Hyperspectral Imaging (HSI) technology in some of these platforms has made possible acquiring large data sets, with high potential in analytical tasks but at the cost of advanced signal processing. In this thesis, effective/efficient feature extraction methods are proposed. Initially, contributions are introduced for efficient computation of the covariance matrix widely used in data reduction methods such as Principal Component Analysis (PCA). By taking advantage of the cube structure in HSI, onsite and real-time covariance computation is achieved, reducing memory requirements as well. Furthermore, following the PCA algorithm, a novel method called Folded-PCA (Fd-PCA) is proposed for efficiency while extracting both global and local features within the spectral pixels, achieved by folding the spectral samples from vector to matrix arrays. Inspired by Empirical Mode Decomposition (EMD) methods, a recent and promising algorithm, Singular Spectrum Analysis (SSA), is introduced to hyperspectral remote sensing, performing extraction of features in the spectral (1D-SSA) and also the spatial (2D-SSA) domain. By successfully suppressing the noise and enhancing the useful signal, more effective feature extraction and data classification are achieved. Furthermore, a fast implementation of the SSA methods is also proposed, leading to reduction of computational complexity. In addition, combination of both spectral- and spatial-domain exploitation is also included, comprising data reduction. Finally, promising Deep Learning (DL) approaches are evaluated by the analysis of Stacked AutoEncoders (SAEs) for feature extraction and data reduction, introducing a method called Segmented-SAE (S-SAE), working in local regions of the spectral domain. Preliminary results have validated its great potential in this context.
|Date of Award||5 Nov 2015|
- University Of Strathclyde
|Supervisor||Jinchang Ren (Supervisor) & Stephen Marshall (Supervisor)|