Superpixel-feature-based multiple kernel sparse representation for hyperspectral image classification

Dan Li; Qiang Wang; Fanqiang Kong

doi:10.1016/j.sigpro.2020.107682

ScienceGate Book Chapters

JOURNAL ARTICLE

Superpixel-feature-based multiple kernel sparse representation for hyperspectral image classification

Dan Li Qiang Wang Fanqiang Kong

Year: 2020 Journal: Signal Processing Vol: 176 Pages: 107682-107682 Publisher: Elsevier BV

DOI: 10.1016/j.sigpro.2020.107682

Get Full-Text PDF Get Analytical Report

Keywords:

Pattern recognition (psychology) Kernel (algebra) Artificial intelligence Hyperspectral imaging Feature (linguistics) Sparse approximation Representation (politics) Computer science Mathematics Support vector machine Feature vector Feature extraction Operator (biology) Pixel

Metrics

Cited By

3.59

FWCI (Field Weighted Citation Impact)

Refs

0.93

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Remote Sensing and Land Use

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Advanced Chemical Sensor Technologies

Physical Sciences → Engineering → Biomedical Engineering

Superpixel-feature-based multiple kernel sparse representation for hyperspectral image classification

Metrics

Citation History

Topics

Related Documents

Adaptive kernel sparse representation based on multiple feature learning for hyperspectral image classification

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Feature extraction based on kernel sparse representation for hyperspectral image classification

Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation

Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation