Change Detection in SAR Images Based on Semi-Supervised Learning

Song Tian; Jian She  Song; Shu Bing Tian; Wei Gong

doi:10.4028/www.scientific.net/AMM.596.484

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 596Change Detection in SAR Images Based on...

Change Detection in SAR Images Based on Semi-Supervised Learning

Abstract:

Support Vector Machine (SVM) is a supervised approach, which needs large numbers of labeled samples. However, it is difficult to obtain such samples for change detection based on SAR images and the available labeled samples are very limited. this paper proposes a semi-supervised support vector machine (S³VM) unsupervised SAR image change detection. Using of K-means clustering method obtain threshold of image; introduce offsets which are automatically selected to achieve a pseudo-training set and unlabeled set; Finally, based on the statistical characteristics of semi-supervised support vector machines for image change and non-change class. The experimental results showed that: In the case without using noise reduction and the reduction in the number of samples, the proposed algorithm can maintain better classification, generalization and more stable detection accuracy.

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Periodical:

Applied Mechanics and Materials (Volume 596)

Pages:

484-489

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.596.484

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Online since:

July 2014

Authors:

Song Tian*, Jian She Song, Shu Bing Tian, Wei Gong

Keywords:

Change Detection, K-Mean, SAR Image, Semi-Supervised Support Vector Machines

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* - Corresponding Author

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