International Science Index


10007588

Markov Random Field-Based Segmentation Algorithm for Detection of Land Cover Changes Using Uninhabited Aerial Vehicle Synthetic Aperture Radar Polarimetric Images

Abstract:

The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

References:
[1] Z. Qi, A. G.-O. Yeh, X. Li, and X. Zhang, “A three-component method for timely detection of land cover changes using polarimetric SAR images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 107, pp. 3-21, 2015.
[2] A. N. French, T. J. Schmugge, J. C. Ritchie, A. Hsu, F. Jacob, and K. Ogawa, “Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities,” Remote Sensing of Environment, vol. 112, pp. 1730-1748, 2008.
[3] M. A. Friedl, D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, et al., “MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets,” Remote Sensing of Environment, vol. 114, pp. 168-182, 2010.
[4] G. Moser and S. B. Serpico, “Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, pp. 2972-2982, 2006.
[5] A. A. Nielsen, H. Skriver, and K. Conradsen, “Complex Wishart Distribution Based Analysis of Polarimetric Synthetic Aperture Radar Data,” in International Workshop on the Analysis of Multi-temporal Remote Sensing Images, 2007, pp. 1-6.
[6] M. Salehi, M. R. Sahebi, and Y. Maghsoudi, “Improving the accuracy of urban land cover classification using Radarsat-2 PolSAR data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, pp. 1394-1401, 2014.
[7] M. Hussain, D. Chen, A. 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, pp. 91-106, 2013.
[8] F. Baselice, G. Ferraioli, and V. Pascazio, “Markovian change detection of urban areas using very high resolution complex SAR images,” IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 995-999, 2014.
[9] S. Z. Li, Markov random field modeling in image analysis: Springer Science & Business Media, 2009, pp.1–3.
[10] S.-E. Park, Y. Yamaguchi, and D.-j. Kim, “Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR,” Remote Sensing of Environment, vol. 132, pp. 212-220, 2013.
[11] Y. Chen and Z. Cao, “An improved MRF-based change detection approach for multitemporal remote sensing imagery,” Signal processing, vol. 93, pp. 163-175, 2013.
[12] T. Kasetkasem and P. K. Varshney, “An image change detection algorithm based on Markov random field models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 1815-1823, 2002.
[13] R. Fjørtoft, A. Lopes, P. Marthon, and E. Cubero-Castan, “An optimal multiedge detector for SAR image segmentation, ” IEEE Transactions on Geoscience and Remote Sensing, vol. 36, pp. 793-802, 1998.
[14] R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Transactions on geoscience and remote sensing, vol. 26, pp. 764-773, 1988.
[15] C. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proceedings-Radar, Sonar and Navigation, vol. 143, pp. 391-399, 1996.
[16] J. Schou, H. Skriver, A. A. Nielsen, and K. Conradsen, “CFAR edge detector for polarimetric SAR images, ”IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 20-32, 2003.
[17] G. Ferraioli, “Multichannel InSAR building edge detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, pp. 1224-1231, 2010.