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Proceedings Paper

Hyperspectral image classification based on EMAPs spatial-spectral features fusion and SMLR
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Paper Abstract

This paper presents a novel classification method of hyperspectral image(HSI) based on EMAPs and SMLR. Firstly, we adopt EMAPs(Extended Morphological multi-Attribute Profiles) algorithm to extract the spatial information of HSI efficiently, and combine the spectral information to form spatial-spectral features fusion model. EMAPs can replace simple structural elements with multiple attributes structure and cascade them to obtain attributes feature of multiple structures. Then we utilize SMLR(Sparse Multinomial Logistic Regression) for HSI classification. SMLR is applicable to high-dimensional and large data sets. A multiclass classifier based on MLR is adopted, and a fast algorithm is used to learn a sparse multiclass classifier. Compared with other methods in HSI experiments, our method provides an excellent result.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080638 (9 August 2018); doi: 10.1117/12.2503193
Show Author Affiliations
Rong Ren, North Minzu Univ. (China)
Hefei Univ. of Technology (China)
Wenxing Bao Sr., North Minzu Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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