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Library Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery

Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery

Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery

Resource information

Date of publication
декабря 2013
Resource Language
ISBN / Resource ID
AGRIS:US201600059984
Pages
66-78

Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins. To fully leverage all of the precious unlabeled data, integration of filtering in a transductive SVM is proposed. Using two labeled image datasets of small size and two large unlabeled image datasets, the effectiveness of the proposed method is explored. Experimental results show that the proposed technique achieves average overall accuracies of around 4.5–7.8%, 0.8–2.6% and 0.9–2.2% more than the standard inductive SVM (ISVM), progressive transductive SVM (PTSVM) and low density separation (LDS) classifiers, respectively on larger domains in case of labeled datasets. Using image datasets, visual interpretation from the classified images as well as the segmentation quality reveal that the proposed method can efficiently filter informative data from the unlabeled samples.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Maulik, Ujjwal
Chakraborty, Debasis

Publisher(s)
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