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Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery
Institution:1. Department of Otolaryngology-Head and Neck Surgery, Croix Rousse Hospital, Hospices Civils de Lyon, Lyon, France;2. Clinical Research Center, Croix Rousse Hospital, Hospices Civils de Lyon, Lyon, France;3. Cancer Research Center of Lyon, UMR Inserm U1052, CNRS 5286, Lyon, France;4. Claude Bernard University, Lyon 1, France
Abstract: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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