access icon free Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images

Dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method for reducing the dimension of hyperspectral image data. First, a new and fast band selection algorithm is proposed for hyperspectral images based on an improved determinantal point process (DPP). To reduce the amount of calculation, the dual-DPP is used for fast sampling representative pixels, followed by k-nearest neighbour-based local processing to explore more spatial information. These representative pixel points are used to construct multiple adjacency matrices to describe the correlation between bands based on mutual information. To further improve the classification accuracy, two-dimensional singular spectrum analysis is used for feature extraction from the selected bands. Experiments show that the proposed method can select a low-redundancy and representative band subset, where both data dimension and computation time can be reduced. Furthermore, it also shows that the proposed dimensionality reduction algorithm outperforms a number of state-of-the-art methods in terms of classification accuracy.

Inspec keywords: geophysical image processing; feature extraction; image classification; hyperspectral imaging; image representation; learning (artificial intelligence); spectral analysis; image sampling

Other keywords: classification accuracy; hyperspectral image data; dual-DPP; fast band selection algorithm; data redundancy; representative pixel points; hyperspectral data processing; k-nearest neighbour; dimensionality reduction algorithm; fast sampling representative pixels; feature extraction; representative band subset; determinantal point process; two-dimensional singular spectrum analysis

Subjects: Geography and cartography computing; Geophysical techniques and equipment; Geophysics computing; Data and information; acquisition, processing, storage and dissemination in geophysics; Knowledge engineering techniques; Computer vision and image processing techniques; Image recognition; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5419
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