融合光谱度量标记迁移和Tri-training的高光谱遥感图像半监督分类算法

doi:10.11959/j.issn.2096-0271.2022084

Abstract

Abstract:

Aimed at the problem that a large number of hyperspectral remote sensing images were rich in spectral and spatial information, and the labeled samples available for image classification were far less than unlabeled samples, a semisupervised spectral-spatial classification algorithm was proposed by fusing spectral measure-based label transfer and Tri-training.A spectral measure-based label transfer method was proposed for our algorithm.The transferred labels and predicted labels for Tri-training algorithm were used to predict the labels of expanded unlabeled samples, which can promoted the prediction accuracies of labels for expanded unlabeled samples.Meanwhile, our algorithm selectel expanded samples based on spatial correlation, and used spectral and spatial features to improve the accuracy of image classification.Experimental study was executed on two public hyperspectral remote sensing image datasets, and the results showed that the proposed algorithm outperform tri-training algorithm.

Key words: classification of hyperspectral image, semi-supervised classification, texture feature, spectral measurement, tri-training algorithm

CLC Number:

TP751

Feng CAO, Wentao LI, Jiancheng LUO, Deyu LI, Yuhua QIAN, Hexiang BAI, Chao ZHANG. Semi-supervised classification algorithm for hyperspectral remote sensing images fusing spectral measure-based label transfer and tri-training[J]. Big Data Research, 2023, 9(6): 72-89.

Figures/Tables 14

References 23

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评价指标	SMT-RSK	SMT-RF	SMT-SVM	SMT-KNN	T-RSK	T-RF	T-SVM	T-KNN
OA	90.81%	88.13%	85.24%	86.40%	81.83%	79.83%	75.44%	77.12%
AA	92.22%	89.58%	86.88%	88.19%	83.25%	81.94%	77.55%	78.49%
Kappa	0.8691	0.8277	0.7892	0.8036	0.7611	0.6888	0.6544	0.6805

评价指标	SMT-RSK	3CLL	CMSI	SFCM	MT	LGC
OA	90.81%	95.64%	85.71%	88.00%	85.27%	87.32%
AA	92.22%	90.17%	88.88%	90.14%	87.41%	—
Kappa	0.8691	—	0.8318	0.883	0.8255	0.8410

评价指标	SMT-RSK	SMT-RF	SMT-SVM	SMT-KNN	T-RSK	T-RF	T-SVM	T-KNN
OA	92.47%	89.88%	87.55%	88.31%	83.59%	81.93%	76.81%	79.56%
AA	93.92%	91.14%	88.96%	90.23%	85.27%	83.11%	77.99%	81.94%
Kappa	0.8874	0.8362	0.8042	0.8186	0.7815	0.6954	0.6713	0.7001

评价指标	SMT-RSK	CMSI	FWA	SDME	SFCM	MT
OA	92.47%	87.68%	87.44%	89.4%	91.35%	66.89%
AA	93.92%	92.14%	—	86.6%	—	73.76%
Kappa	0.8874	0.8283	0.83	—	0.8974	0.5802

数据集		SM			T-RSK			SMT-RSK
数据集	OA	AA	Kappa	OA	AA	Kappa	OA	AA	Kappa
India Pines	71.42%	75.33%	0.6919	81.83%	83.25%	0.7611	90.81%	92.22%	0.8691
Pavia University	75.67%	78.41%	0.7178	83.59%	85.27%	0.7815	92.47%	93.92%	0.8874

Semi-supervised classification algorithm for hyperspectral remote sensing images fusing spectral measure-based label transfer and tri-training

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Figures/Tables 14

References 23

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