Open AccessRobust multi‐view representation for spatial–spectral domain in application of hyperspectral image classification
Author(s) -
Li Yanshan,
Wang Xianchen,
Huang Qinghua,
Hu Xiaohui,
Xie Weixin
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5112
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , locality , computer science , robustness (evolution) , pyramid (geometry) , affine transformation , representation (politics) , spatial analysis , mathematics , computer vision , philosophy , linguistics , biochemistry , chemistry , geometry , statistics , politics , political science , pure mathematics , law , gene
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.