Abstract
Change detection for remote sensing images is very important for urban planning, disaster evaluation etc. Traditional detection methods include supervised and unsupervised learning algorithm. A novel semi-supervised multiscale change detection method based on online learning framework is presented in this paper. Firstly, mean-variance classifier and SVM classifier are trained at the different scales of 2*2 pixels block and original pixel respectively. Initial training set is extracted from the ground truth. Secondly, the difference image is obtained according to two phase remote sensing images, and arranged by the unit of 16*16 pixel block. Image blocks are input into the mean-variance classifier and SVM classier to be detected one by one, it is cascade connection between two classifiers. The error correction rules are used to choose the misclassified instances to retrain the classifiers. Experiment results show that the method in this paper can efficiently decrease the FN (false negative numbers) to improve the performance of change detection algorithm.
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Acknowledgments
This paper was supported by the Fundamental Research Funds for the Central Universities (K5051202048) and the National Natural Science Foundation Project (61172146).
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Zhang, J., Zhai, J. (2015). Multiscale Change Detection Method for Remote Sensing Images Based on Online Learning Framework. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_33
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DOI: https://doi.org/10.1007/978-3-319-23989-7_33
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