access icon free Robust multi-view representation for spatial–spectral domain in application of hyperspectral image classification

Spatial–spectral representation plays an important role in hyperspectral images (HSIs) classification. However, many of the existing local feature algorithms for HSIs are based on the two-dimensional image and do not take full advantage of the information hidden in HSI, such as spatial–spectral locality correlation information, thereby reducing the robustness of these algorithms. In response to these problems, this study presents a robust multi-view spatial–spectral representation method with the characteristics of HSIs. There are two key techniques in this representation method, called spatial–spectral locality constrained linear coding (SSLLC) and spatial–spectral pyramid matching model (SSPM). Firstly, SSLLC applies the locality information of the feature points and visual words and uses the discriminant information provided by the nearest-neighbouring spatial–spectral feature points in HSIs. Secondly, SSPM works by partitioning the image into increasingly fine sub-cubes and uses the cubes to match the local features of the HSIs. The multi-view representation is tolerant to illumination change, image rotation, affine distortion etc. To assess the validity of authors' algorithm, the authors compared their results with several existing approaches, including a deep learning method. The experimental results show that this representation method can effectively improve the accuracy of HSIs classification.

Inspec keywords: feature extraction; image classification; geophysical image processing; geophysical techniques; image matching; image representation; learning (artificial intelligence)

Other keywords: spatial dimensions; spectral dimensions; robust multiview representation; spatial-spectral locality correlation information; image rotation; local feature algorithms; nearest-neighbouring spatial-spectral feature points; discriminant information; spatial-spectral domain; deep learning method; two-dimensional image; robust multiview spatial-spectral representation method; HSI classification; spatial-spectral pyramid matching model; locality information; hyperspectral image classification; local features; spatial-spectral locality

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

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2018.5112
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content/journals/10.1049/iet-cvi.2018.5112
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