A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM

To improve the accuracy and generalization ability of hyperspectral image classification, a feature extraction method integrating principal component analysis (PCA) and local binary pattern (LBP) is developed for hyperspectral images in this article. The PCA is employed to reduce the dimension of th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.2781-2795
Main Authors: Chen, Huayue, Miao, Fang, Chen, Yijia, Xiong, Yijun, Chen, Tao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
Computer applications
Datasets
Dimensions
Feature extraction
Gray wolf optimization (GWO)
hyperspectral image classification
Hyperspectral imaging
Image classification
Internet of Things
Kernel
kernel extreme learning machine (KELM)
Learning algorithms
local binary pattern (LBP)
Machine learning
Optimization
Principal component analysis
principal component analysis (PCA)
Principal components analysis
Training
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To improve the accuracy and generalization ability of hyperspectral image classification, a feature extraction method integrating principal component analysis (PCA) and local binary pattern (LBP) is developed for hyperspectral images in this article. The PCA is employed to reduce the dimension of the spectral features of hyperspectral images. The LBP with low computational complexity is used to extract the local spatial texture features of hyperspectral images to construct multifeature vectors. Then, the gray wolf optimization algorithm with global search capability is employed to optimize the parameters of kernel extreme learning machine (KELM) to construct an optimized KELM model, which is used to effectively realize a hyperspectral image classification (PLG-KELM) method. Finally, the Indian pines dataset, Houston dataset, and Pavia University dataset and an application of WHU-Hi-LongKou dataset are selected to verify the effectiveness of the PLG-KELM. The comparison experiment results show that the PLG-KELM can obtain higher classification accuracy, and takes on better generalization ability for small samples. It provides a new idea for processing hyperspectral images.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3059451