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Extending method of remote sensing image training sample based on semi-supervised learning in both time and spatial domain |
| REN Guangbo, ZHANG Jie, MA Yi, SONG Pingjian |
| First Institute of Oceanography, SOA, Qingdao 266061, China |
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Abstract In classification of remote sensing images without any training samples, the choice of training samples from other representative images might be the only direct way; nevertheless, due to the difference of radiometric environments, the classification training samples from one image could not be well representative of other images. It is known that labeled samples from one image may not be effective for classifying others with high accuracy. In view of the above problem, a novel semi-supervised transcductive support vector machine(TSVM)method is proposed. The authors first chose a large quantities of unlabeled samples from the images which need to be classified in an unsupervised way, then extracted the inherent construction information of different classes in the feature space. Next, with the help of semi-supervised learning theory, the authors trained a classifier which was pre-trained by the labeled samples from another image in a recursive way, and at last an optimized classifier was obtained. It should be noted that two images involved in the method must have familiar land covers and acquired times. Classification experiments of SPOT5 and QuickBird remote sensing images were undertaken by the authors, and the classification results prove that the method proposed in this paper can effectively realize the sample extending application in both time and spatial domain.
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| Keywords
CBERS-02B satellite
HJ-1 satellite
BJ-1 satellite
macroscopic monitoring
land use status
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Issue Date: 28 April 2013
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2013,
Vol. 25
