Remote sensing image classification based on hybrid entropy and L1 norm
Abstract：Aiming at remote sensing image data having properties of high-dimension, nonlinearity, and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entroy and Ll-norm was proposed. At first, a hybrid entroy was designed by combining quasi-entropy with entropy difference, which was used to select the most 'valuable' unlabeled samples from the massive unlabeled sample set. In the second step, a L~-norm distance mectric was used to further select and to remove outliers and redundant data from the most 'valuable' unlabeled samples. At last, the original labeled and the selected unlabeled samples were adopted to train the PLSSVM. Experimental results on ROSIS hyperspectral remote sensing image show that the overall accuracy and Kappa coeffi- cient of the proposed classification method reach 89.90% and 0. 868 5 respectively. The pro- posed method can obtain higher classification accuracy with few training samples, which is much applicable for classification problem of remote sensing image.
Keywords：remote sensing imagehybrid entropyL1 normactive learningprobability leastsquares support vector machine
Publication Date：2012-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 971-977 )