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

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.

Keywords

• classification of hyperspectral image;semi-supervised classification;texture feature;spectral measurement;tri-training algorithm