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Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm
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 Title & Authors
Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm
Jaewan, Choi;
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 Abstract
The change detection algorithms, based on remotely sensed satellite imagery, can be applied to various applications, such as the hazard/disaster analysis and the land monitoring. However, unchanged areas sometimes detected as the changed areas due to various errors in relief displacements and noise pixels, included in the original multi-temporal dataset at the application of unsupervised change detection algorithm. In this research, the object-based changed detection for the high-spatial resolution satellite images is applied by using the IR-MAD (Iteratively Reweighted- Multivariate Alteration Detection), which is one of those representative change detection algorithms. In additionally, we tried to increase the accuracy of change detection results with using the additional information, based on the cross-sharpening method. In the experiment, we used the KOMPSAT-2 satellite sensor, and resulted in the object-based IR-MAD algorithm, representing higher changed detection accuracy than that by the pixel-based IR-MAD. Also, the object-based IR-MAD, focused on cross-sharpened images, increased in accuracy of changed detection, compared to the original object-based IR-MAD. Through these experiments, we could conclude that the land monitoring and the change detection with the high-spatial-resolution satellite imagery can be accomplished efficiency by using the object-based IR-MAD algorithm.
 Keywords
High-spatial Resolution Satellite Imagery;Object-based Change Detection;Cross-sharpened Images;IR-MAD;
 Language
Korean
 Cited by
 References
1.
Aleksandrowicz S., Turlej K., Lewiński S., and Bochenek Z. (2014), Change detection algorithm for the production of land cover change maps over the European Union Countries, Remote Sensing, Vol. 6, No. 7, pp. 5976-5994. crossref(new window)

2.
Byun, Y., Han, Y., and Chae, T. (2013), A multispectral image segmentation approach for objec-based image classification of high resolution satellite imagery, KSCE Journal of Civil Engineering, Vol. 17, No 2, pp. 486-497. crossref(new window)

3.
Choi. J. and Byun. Y. (2012), Effects analysis of the image fusion result by a relief displacement and changed area, in Proc. KSGPC, 2012, pp. 303–304.

4.
Carvalho Junior, O. A., Guimaraes, R. F., Gillespie, A. R., Silva, N. C., and Gomes, R. A. T. (2011), New approach to change vector analysis using distance and similarity measures, Remote Sensing, Vol. 3, No. 11, pp. 2473-2493. crossref(new window)

5.
Chen, G., Hay, G. J., Carvalho, L. M. T., and Wulder, M. A. (2012), Object-based change detection, International Journal of Remote Sensing, Vol. 33, No. 14, pp. 4434-4457. crossref(new window)

6.
Erturk, A. and Plaza, A. (2015), Informative change detection by unmixing for hyperspectral images, IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 6, pp. 1252-1256. crossref(new window)

7.
Kim, D. and Kim, H. (2008), Automatic thresholding method using cumulative similarity measurement for unsupervised change detection of multispectral and hyperspectral images, Korean Journal of Remote Sensing, Vol. 24, No. 4, pp. 341-349. (in Korean with English abstract)

8.
Marchesi, S., Bovolo, F., and Bruzzone, L. (2010), Context-sensitive technique robust to registration noise for change detection in VHR multispectral images, IEEE Transactions on Image Processing, Vol. 19, No. 7, pp. 1877-1889. crossref(new window)

9.
Marpu, P. R., Gamba, P., and Canty, M. J. (2011), Improving change detection results of IR-MAD by eliminating strong changes, IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 4, pp. 799-803. crossref(new window)

10.
Nielsen, A. A. (2007), The regularized iteratively reweighted MAD method for change detection in multiand hyperspectral data, IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 463-478. crossref(new window)

11.
Park, N., Chi, K., Lee K., and Kwon, B. (2003), Automatic estimation of threshold values for change detection of multi-temporal remote sensing images, Korean Journal of Remote Sensing, Vol. 19, No. 6, pp. 465-478. (in Korean with English abstract)

12.
Wang, B., Choi, S., Choi, J., and Yang, S. (2013), Comparison of change detection accuracy based on VHR images corresponding to the fusion estimation indexes, Journal of the Korean Society for Geospatial Information System, Vol. 21, No. 2, pp. 63-69. (in Korean with English abstract)

13.
Wang, B., Choi, S., Byun, Y., Lee, S., and Choi, J. (2015a), Object-based change detection of very high resolution satellite imagery using the cross-sharpening of multitemporal data, IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 5, pp. 1151-1155. crossref(new window)

14.
Wang, B., Choi, S., Han, Y., Lee, S., and Choi, J. (2015b), Application of IR-MAD using synthetically fused images for change detection in hyperspectral data, Remote Sensing Letters, Vol. 6, No. 8, pp. 578-586. crossref(new window)

15.
Wu, C., Du, B., and Zhang, L. (2013), A subspace-based change detection method for hyperspectral images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 2, pp.815-830. crossref(new window)