NDLI: Robust multi-view representation for spatial–spectral domain in application of hyperspectral image classification

Content Provider	IET Digital Library
Author	Li, Yanshan Wang, Xianchen Huang, Qinghua Hu, Xiaohui Xie, Weixin
Abstract	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.
Starting Page	90
Ending Page	96
Page Count	7
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5112
Journal	IET Computer Vision
Publisher Date	2018-08-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Deep Learning Method Discriminant Information Feature Extraction Geography And Cartography Computing Geophysical Image Processing Geophysical Technique And Equipment Geophysics Computing HSI Classification Hyperspectral Image Classification Image Classification Image Matching Image Recognition Image Representation Image Rotation Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Learning in AI Local Feature Local Feature Algorithm Locality Information Nearest-neighbouring Spatial-spectral Feature Point Robust Multiview Representation Robust Multiview Spatial-spectral Representation Method Spatial Dimensions Spatial-spectral Domain Spatial-spectral Locality Spatial-spectral Locality Correlation Information Spatial-spectral Pyramid Matching Model Spectral Dimensions Two-dimensional Image
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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