Synthetic Aperture Radar Remote Sensing Classification Using the Bag of Visual Words Model to Land Cover Studies
Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474535Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 266
 Li, X., Zhang, L., Wang, L., & Wan, X., “Effects of BOW Model with Affinity Propagation and Spatial Pyramid Matching on Polarimetric SAR Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
 Feng, J., Jiao, L. C., Zhang, X., & Niu, R., “An effective bag-of-visual-words framework for SAR image classification,” In Proc. of SPIE Vol (Vol. 8006, pp. 800606-1), Nov, 2011.
 Amrani, M., Chaib, S., Omara, I., & Jiang, F., “Bag-of-visual-words based feature extraction for SAR target classification,” In Ninth International Conference on Digital Image Processing (ICDIP 2017) (Vol. 10420, p. 104201J). International Society for Optics and Photonics, July, 2017.
 Sivic, Josef, and A. Zisserman. "Video Google: A text retrieval approach to object matching in videos." The Ninth IEEE International Conference on Computer Vision (ICCV), p. 1470, 2003.
 Sivic, Josef, Bryan C. Russell, Alexei A. Efros, A. Zisserman, and William T. Freeman. "Discovering object categories in image collections." 2005.
 Xu, S., Fang, T., Huo, H., & Li, D., “A novel method of aerial image classification based on attention-based local descriptors,” Procedia Earth and Planetary Science, 1(1), 1133-1139, 2009.
 Zhaoa, L. J., Huoa, L. Z., & Tang, P., “A bag-of-visual-words model based framework for object-oriented land-cover classification,” In Proc. of SPIE Vol (Vol. 9260, pp. 92603S-1), Nov, 2014.
 Salehi, M., Sahebi, M. R., & Maghsoudi, Y., “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, 7(4), 1394-1401, 2014.
 R. M. Barnes, “Roll invariant decompositions for the polarization covariance matrix,” in Proc. Polarimetric Technology Workshop, Redstone Arsenal, Alabama, USA, 1988.
 D. L. Evans, T. G. Farr, J. J. Van Zyl, and H. A. Zebker, “Radar polarimetry: Analysis tools and applications,” IEEE Trans. Geosci. Remote Sens., vol. 26, no. 6, pp. 774–789, 1988.
 Uhlmann, S., & Kiranyaz, S., “Integrating color features in polarimetric SAR image classification,” IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2197-2216, 2014.
 E. Krogager, “New decomposition of the radar target scattering matrix,” Electron. Lett. vol. 26, pp. 1525–1527, 1990.
 S. R. Cloude and E. Pottier, “An entropy based classification scheme for land applications of polarimetric SAR,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 1, pp. 68–78, Jan. 1997.
 A. Freeman and S. L. Durden, “A three-component scattering model for polarimetric SAR data,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963–973, 1998.
 Haddadi G, A., Sahebi, M. R., & Mansourian, A., “Polarimetric SAR feature selection using a genetic algorithm”. Canadian Journal of Remote Sensing, 37(1), 27-36, 2011.