Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 143 > pp. 519-544


By B. Mishra and J. Susaki

Full Article PDF (1,896 KB)

In this research paper, we propose supervised and unsupervised change detection methodologies focused on the analysis of multitemporal Synthetic Aperture Radar (SAR) images. These approaches are based on three main steps: (1) a comparison of multitemporal image was carried out by normalized difference ratio (NDR) operator; (2) implementing a novel supervised or unsupervised thresholding and (3) generating the change map by coupling of thresholding along with a region growing algorithm. In the first step, the two filtered multitemporal images were used to generate NDR image that was subjected to analysis. In the second step, by assuming a Gaussian distribution in the nochange area, we identified the pixel range that fits the Gaussian distribution better than any other range iteratively to detect the no-change area that eventually separates the change areas. In the supervised method, several sample no-change pixels were selected and the mean (μ) and the standard deviation (σ) were obtained. Then, μ±3σ was applied to select the best threshold values. Finally, a traditional thresholding algorithm was modified and implemented with the coupling of the region growing algorithm to consider the spatial information to generate the change map. The Gaussian distribution was assumed because it better fits the conditional densities of the no-change class in the NDR image. The effectiveness of the proposed methods was verified with the simulated images and the real images associated to geographical locations. The results were compared with the manual trial and error procedure (MTEP) and traditional unsupervised expectation-maximization (EM) method. Both proposed methods gave similar results with MTEP and significant improvement in Kappa coefficient in comparison to the traditional EM method was found in both cities. The coupling of the modified thresholding with the region growing algorithm is very effective with all methods.

B. Mishra and J. Susaki, "Coupling of Thresholding and Region Growing Algorithm for Change Detection in SAR Images," Progress In Electromagnetics Research, Vol. 143, 519-544, 2013.

1. Lunetta, R. S., J. F. Knight, J. Ediriwickrema, J. G. Lyon, and L. D. Worthy, "Land-cover change detection using multi-temporal MODIS NDVI data ," Remote Sens. of Envt., Vol. 105, No. 2, 142-154, 2006.

2. Moser, G. and S. B. Serpico, "Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery ," IEEE Trans. Geosci. Remote Sens., Vol. 44, No. 10, 2972-2982, 2006.

3. Bazi, Y., L. Bruzzone, and F. Melgani, "An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images ," IEEE Trans. Geosci. Remote Sens., Vol. 43, No. 4, 874-887, 2005.

4. Liao, M., L. Jiang, H. Lin, B. Buang, and J. Gong, "Urban change detection based on coherence and intensity characteristics of SAR imagery ," Photogrammetric Engineering & Remote Sensing, Vol. 74, No. 8, 999-1006, 2008.

5. Ban, Y. and O. A. Yousif, "Multitemporal space borne SAR data for urban change detection in China," IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, No. 4, 1087-1094, 2012.

6. Rignot, E. J. M. and J. J. Van Zyl, "Change detection techniques for ERS-1 SAR data," IEEE Trans. Geosci. Remote Sens., Vol. 31, No. 4, 896-896, 1993.

7. Pacifici, F., F. Del Frate, C. Solimini, and W. J. Emery, "An innovative neural-net method to detect temporal changes in high highresolution ," IEEE Trans. Geosci. Remote Sens., Vol. 45, No. 9, 2940-2952, 2007.

8. Uprety, P. and F. Yamazaki, "Detection of building damage in the 2010 Haiti earthquake using high-resolution SAR intensity images ," J. Japan Asso. for Earthquake Engineering, Vol. 6, 21-35, 2012.

9. Ramesh, N., J. H. Yoo, and I. K. Sethi, "Thresholding based on histogram approximation," IEEE Proc. — Vis. Image Signal IEEE Proc. — Vis. Image Signal, Vol. 142, No. 5, 271-279, 1995.

10. Kittler, J. Illingworth and J. Illingworth, "Minimum error thresholdin," Pattern Recognit., Vol. 19, No. 1, 41-47, 1986.

11. Albregtsen, F., "Nonparametric histogram thresholding methods — Error versus relative object area," Proc. Eighth Scandinavian Conf. Image Analysis, 273-280, 1993.

12. Bazi, Y., L. Bruzzone, and F. Melgani, "Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images ," IEEE Geosci. and Remote Sens. Letters, Vol. 3, No. 2, 349-353, 2006.

13. Dekker, R. J., "Speckle filtering in satellite SAR change detection imagery," Int. J. Remote Sens., Vol. 19, No. 6, 1133-1146, 1998.

14. Im, J. and J. R. Jensen, "A change detection model based on neighborhood correlation image analysis and decision tree classification ," Remote Sens. of Envt., Vol. 99, No. 3, 326-340, 2005.

15. Cannavacciuolo, L., Cannavacciuolo, L., G. Moser, W. Emery, and S. B. Serpico, "A contextual change detection method for high-resolution optical images of urban areas," Urban Remote Sensing Joint Event, 1-7, 2007.

16. Hussain, M., D. Chen, A. Chen, H. Cheng, H. Wei, and D. Stanley, "Change detection from remotely sensed images: From pixel-based to object-based approaches," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 80, 91-106, 2013.

17. Yasnoff, W. A., J. K. Mui, and J. W. Bacus, "Error measures for scene segmentation," Pattern Recognit., Vol. 9, No. 4, 217-231, 1977.

18. Mishra, P. and D. Singh, "Land cover classification of PALSAR images by knowledge based decision tree classifier and supervised classifiers based on SAR observables ," Progress In Electromagnetics Research B, Vol. 30, 47-70, 2011.

19. Adams, R. and L. Bischof, "Seeded region growing on Pattern Analysis and Machine Intelligence," IEEE Trans., Vol. 16, No. 6, 641-647, 1994.

20. Congalton, R. G. and K. Green, Assessing the Accuracy of Remotely Sensed Data: Principals and Practices, CRC Press, 2009.

21. Foody, G. M., "Accessing the accuracy of land cover change with imperfect ground reference data," Remote Sens. of Envt., Vol. 114, No. 10, 2271-2283, 2010.

22. Inglada, J. and G. Mercier, "A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis ," IEEE Trans. Geosci. Remote Sens., Vol. 45, No. 5, 1432-1445, 2007.

23., "World Population Prospects, The 2012 Revision, ,", 2012.

24. Helsel, D. R. and R. M. Hirsch, Statistical Methods in Water Resources Techniques of Water Resources Investigations, Book 4, Chapter A3, US Geological Survey, 2002.

© Copyright 2014 EMW Publishing. All Rights Reserved