Abstract
Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. AdaBoost is an effective approach to fi...
Highlights
Land cover can really reflect the information of the earth’s surface coverage, which is closely related with human production and daily life
The results showed that boosting algorithm can improve the classification performance of support vector machines (SVMs) for land cover classification.[20]
An ensemble SVMs model using an AdaBoost algorithm called as AdaPSVMs is presented to reduce the impact of this issue
Summary
Land cover can really reflect the information of the earth’s surface coverage, which is closely related with human production and daily life. The FCM clustering algorithm is used for obtaining more reliable and non-noisy training samples based on membership values of different clusters. SVMs are considered as base classifiers, where RBF kernel function is used in the proposed model. The FCM algorithm is introduced to detect the noises based on the movement of the center for clustering in the training dataset. The article uses kappa coefficient as evaluation index except the total classification accuracy, and the formula is shown as kappa 1⁄4 N PNri1⁄42 1ÀxiP i Àri1⁄4P1ðrix1⁄4i1+ðxxi++ixÞ+ iÞ ð16Þ where r is the number of error matrix rows, xii is the value of ith row and ith column, xi + is the sum of the ith row, x + i is the sum of the ith column, and N is the total number of samples. 418 371 237 389 364 1779 training (2059 samples) and the other for testing (1779 samples)
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