Improving the Segmentation of Polarimetric Images with a Combined Approach of Region-Based and Boundary-Based Techniques

Document Type : Original Article

Authors

1 Master Student of Remote Sensing, Faculty of Geodesy & Geomatics Engineering , K.N. Toosi University of Technology

2 Associate Prof., Faculty of Geodesy & Geomatics Engineering , K.N. Toosi University of Technology

3 Ph.D. Student of Remote Sensing, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology

Abstract

Synthetic aperture radar (SAR) sensors with various properties offer potential in various remote sensing applications, such as land cover and land use segmentation. Despite the two independent approaches of region-based segmentation and boundary-based segmentation, it isn't easy to obtain satisfactory results if either process is used in SAR images. In contrast, complementary information can be obtained using both region-based and boundary-based segmentation methods, removing existing limitations and improving results.
In this research, with the help of polarimetric SAR images, a new segmentation method is presented, aiming to improve segmentation results by combining the two region-based and boundary-based approaches. From the set of superpixel methods, the Felzenszwalb method as a proposed region-based algorithm is compared with Quickshift and SLIC methods. The proposed method was able to prevent over-segmentation of the image and significantly increased the efficiency of segmentation analysis. Also, as the proposed method of boundary-based segmentation, Shannon entropy has considerably preserved the boundaries of the image segmentation compared to the two gradient-based methods, Canny and Laplacian. Comparison of the results of this method with reference data shows the total error of 10.39% and 11.25% for the first and second-time images, respectively. Compared to the performance of the other two methods, the absolute error has been decreased to 5.81% and 9.73% in the first image, and 11.16% and 13.86% in the second image, respectively. Finally, as a significant achievement of this research, integrating the two proposed segmentation algorithms improves the accuracy of polarimetric image segmentation.
 

Keywords

References

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P. & Süsstrunk, S., 2012, SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), PP. 2274-2282.
Bovik, A.C., 1988, On Detecting Edges in Speckle Imagery, IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(10), PP. 1618-1627.
Buono, A., Nunziata, F., Migliaccio, M., Yang, X. & Li, X., 2017, Classification of the Yellow River Delta Area Using Fully Polarimetric SAR Measurements, International Journal of Remote Sensing, 38(23), PP. 6714-6734.
Cheng-Xin, Y., Nong, S., Tian-Xu, Z. & Kun, Z., 2005, Image Transition Region Extraction and Segmentation Based on Local Complexity, J. Infrared Millim. Waves, 24(4), PP. 312-316.
Falah, R.K., Bolon, P. & Cocquerez, J.P., 1994, A Region-Region and Region-Edge Cooperative Approach of Image Segmentation, Proceedings of 1st International Conference on Image Processing, IEEE.
Felzenszwalb, P.F. & Huttenlocher, D.P., 2004, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, 59(2), PP. 167-181.
Fjortoft, R., Lopes, A., Marthon, P. & Cubero-Castan, E., 1998, An Optimal Multiedge Detector for SAR Image Segmentation, IEEE Transactions on Geoscience and Remote Sensing, 36(3), PP. 793-802.
Kiani, A. & Sahebi, M.R., 2015, Edge Detection Based on the Shannon Entropy by Piecewise Thresholding on Remote Sensing Images, IET Computer Vision, 9(5), PP. 758-768.
Lee, J.-S., Grunes, M.R. & De Grandi, G., 1999, Polarimetric SAR Speckle Filtering and Its Implication for Classification, IEEE Transactions on Geoscience and Remote Sensing, 37(5), PP. 2363-2373.
Lee, J.-S. & Pottier, E., 2017, Polarimetric Radar Imaging: From Basics to Applications, CRC Press.
 
Li, Z. & Liu, C., 2009, Gray Level Difference-Based Transition Region Extraction and Thresholding, Computers & Electrical Engineering, 35(5), PP. 696-704.
Li, Z., Liu, G., Zhang, D. & Xu, Y., 2016, Robust Single-Object Image Segmentation Based on Salient Transition Region, Pattern Recognition, 52, PP. 317-331.
Liu, M.-Y., Tuzel, O., Ramalingam, S. & Chellappa, R., 2011, Entropy Rate Superpixel Segmentation, CVPR 2011, IEEE.
Liu, M., Zhang, H., Wang, C. & Wu, F., 2014, Change Detection of Multilook Polarimetric SAR Images Using Heterogeneous Clutter Models, IEEE Transactions on Geoscience and Remote Sensing, 52(12), PP. 7483-7494.
Marino, A., 2013, A Notch Filter for Ship Detection with Polarimetric SAR Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), PP. 1219-1232.
Muñoz, X., Freixenet, J., Cufı, X. & Martı, J., 2003, Strategies for Image Segmentation Combining Region and Boundary Information, Pattern Recognition Letters, 24(1-3), PP. 375-392.
Nussbaum, S. & Menz, G., 2008, Object-Based Image Analysis and Treaty Verification: New Approaches in Remote Sensing-Applied to Nuclear Facilities in Iran, Springer Science & Business Media.
Oliver, C. & Lombardo, P., 1996, Simultaneous Mean and Texture Edge Detection in SAR Clutter, IEE Proceedings-Radar, Sonar and Navigation, 143(6), PP. 391-399.
Omati, M. & Sahebi, M.R., 2018, Change Detection of Polarimetric SAR Images Based on the Integration of Improved Watershed and MRF Segmentation Approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11), PP. 4170-4179.
Qi, Z., Yeh, A.G.-O., Li, X. & Zhang, X., 2015, A Three-Component Method for Timely Detection of Land Cover Changes Using Polarimetric SAR Images, ISPRS Journal of Photogrammetry and Remote Sensing, 107, PP. 3-21.
Rezaeian, A., Homayouni, S. & Safari, A., 2015, Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), P. 613.
Schou, J., Dierking, W. & Skriver, H., 2000, Tensor Based Structure Estimation in Multi-Channel Images, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium, Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment, Proceedings (Cat. No. 00CH37120), IEEE.
Schou, J., Skriver, H., Nielsen, A.A. & Conradsen, K., 2003, CFAR Edge Detector for Polarimetric SAR Images, IEEE Transactions on Geoscience and Remote Sensing, 41(1), PP. 20-32.
Shen, J., Du, Y., Wang, W. & Li, X., 2014, Lazy Random Walks for Superpixel Segmentation, IEEE Transactions on Image Processing, 23(4), PP. 1451-1462.
Singh, S. & Talwar, R., 2013, Effects of Topographic Corrections on MODIS Sensor Satellite Imagery of Mountainous Region 2013, International Conference on Signal Processing and Communication (ICSC), IEEE.
Touzi, R., Lopes, A. & Bousquet, P., 1988, A Statistical and Geometrical Edge Detector for SAR Images, IEEE Transactions on Geoscience and Remote Sensing, 26(6), PP. 764-773.
Vedaldi, A. & Soatto, S., 2008, Quick Shift and Kernel Methods for Mode Seeking, European Conference on Computer Vision, Springer.
Wu, Y., Ji, K., Yu, W. & Su, Y., 2008, Region-Based Classification of Polarimetric SAR Images Using Wishart MRF, IEEE Geoscience and Remote Sensing Letters, 5(4), PP. 668-672.
Zhang, Y., Zhang, J., Zhang, X., Wu, H. & Guo, M., 2015, Land Cover Classification from Polarimetric SAR Data Based on Image Segmentation and Decision Trees, Canadian Journal of Remote Sensing, 41(1), PP. 40-50.

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