Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

被引：1

作者：

Li, Na ^{[1
]}

Wang, Ruihao ^{[1
]}

Zhao, Huijie ^{[1
]}

Wang, Mingcong ^{[1
]}

Deng, Kewang ^{[1
]}

Wei, Wei ^{[2
]}

机构：

[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China

[2] Beijing Mech & Elect Engn Design Inst, Beijing 100854, Peoples R China

来源：

SENSORS | 2019年 / 19卷 / 24期

基金：

中国国家自然科学基金;

关键词：

hyperspectral image classification; small sample size; diverse density; sparse representation;

D O I：

10.3390/s19245559

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

引用

页数：12

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