A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

International Journal of Applied Earth Observation and Geoinformation

Volumn 58, Issue , 2017, Pages 201-212

  Tamiminia, Haifa ^a   Homayouni, Saeid ^b   McNairn, Heather ^c   Safari, Abdoreza ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

^b UNIVERSITY OF OTTAWA   (Canada)

^c OTTAWA RESEARCH AND DEVELOPMENT CENTRE   (Canada)

Author keywords

Crop mapping; Kernel based C means clustering; Multi temporal; Polarimetric decompositions; Polarimetric SAR data

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; CANOPY; CLUSTER ANALYSIS; CROP; PHENOLOGY; RESOLUTION; SATELLITE DATA; SATELLITE IMAGERY; SYNTHETIC APERTURE RADAR; TEMPORAL ANALYSIS; VEGETATION MAPPING;

CANADA; MANITOBA; WINNIPEG;

EID: 85041470067     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2017.02.010     Document Type: Article

References (39)

1. Alberga, V., Satalino, G., Staykova, D., Comparison of polarimetric SAR observables in terms of classification performance. Int. J. Remote Sens. 29 (2008), 4129–4150.
2. Atwood, D.K., Small, D., Gens, R., Improving PolSAR land cover classification with radiometric correction of the coherency matrix. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5 (2012), 848–856.
3. Ball, G.H., Hall, D.J., A clustering technique for summarizing multivariate data. Behav. Sci. 12 (1967), 153–155.
4. Blaes, X., Vanhalle, L., Defourny, P., Efficiency of crop identification based on optical and SAR image time series. Remote Sens. Environ. 96 (2005), 352–365.
5. Cable, J.W., Kovacs, J.M., Jiao, X., Shang, J., Agricultural monitoring in northeastern Ontario, Canada, using multi-temporal polarimetric RADARSAT-2 data. Remote Sens. 6 (2014), 2343–2371.
6. Camps-Valls, G., Bruzzone, L., Kernel Methods for Remote Sensing Data Analysis. 2009, John Wiley & Sons.
7. Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Rojo-Álvarez, J.L., Martínez-Ramón, M., Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans. Geosci. Remote Sens. 46 (2008), 1822–1835.
8. Chen, S., Zhang, D., Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34 (2004), 1907–1916.
9. Cloude, S.R., Pottier, E., A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 34 (1996), 498–518.
10. Dave, R.N., Characterization and detection of noise in clustering. Pattern Recognit. Lett. 12 (1991), 657–664.
11. De Oliveira, J.V., Pedrycz, W., Advances in Fuzzy Clustering and Its Applications. 2007, Wiley Online Library.
12. Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C., Harley, R.G., Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12 (2008), 171–195.
13. Entezari, I., Motagh, M., Mansouri, B., Comparison of the performance of L-band polarimetric parameters for land cover classification. Can. J. Remote Sens. 38 (2012), 629–643.
14. Ferreira, M.R., de Carvalho, F.d.A., Kernel-based hard clustering methods in the feature space with automatic variable weighting. Pattern Recogn. 47 (2014), 3082–3095.
15. Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., Jarvis, I., AAFC annual crop inventory, Agro-Geoinformatics (Agro-Geoinformatics). 2013 Second International Conference on. IEEE, 2013, 270–274.
16. Graves, D., Pedrycz, W., Performance of kernel-based fuzzy clustering. Electron. Lett. 43 (2007), 1445–1446.
17. Hoekman, D.H., Vissers, M.A., Tran, T.N., Unsupervised full-polarimetric SAR data segmentation as a tool for classification of agricultural areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4 (2011), 402–411.
18. Jiao, X., Kovacs, J.M., Shang, J., McNairn, H., Walters, D., Ma, B., Geng, X., Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data. ISPRS J. Photogramm. Remote Sens. 96 (2014), 38–46.
19. Keller, J., Krisnapuram, R., Pal, N.R., Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. 2005, Springer Science & Business Media.
20. Kennedy, J., Particle Swarm Optimization, Encyclopedia of Machine Learning. 2010, Springer, 760–766.
21. Kovács, F., Legány, C., Babos, A., Cluster validity measurement techniques, 6th International symposium of hungarian researchers on computational intelligence. Citeseer, 2005.
22. Lee, J.-S., Grunes, M.R., Pottier, E., Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR. IEEE Trans. Geosci. Remote Sens. 39 (2001), 2343–2351.
23. Lee, J.-S., Grunes, M.R., Pottier, E., Ferro-Famil, L., Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Trans. Geosci. Remote Sens. 42 (2004), 722–731.
24. Lee, J.-S., Pottier, E., Polarimetric Radar Imaging: from Basics to Applications. 2009, CRC press.
25. Lopez-Sanchez, J.M., Cloude, S.R., Ballester-Berman, J.D., Rice phenology monitoring by means of SAR polarimetry at X-band. IEEE Trans. Geosci. Remote Sens. 50 (2012), 2695–2709.
26. McNairn, H., Hochheim, K., Rabe, N., Applying polarimetric radar imagery for mapping the productivity of wheat crops. Can. J. Remote Sens. 30 (2004), 517–524.
27. McNairn, H., Jackson, T.J., Wiseman, G., Belair, S., Berg, A., Bullock, P., Colliander, A., Cosh, M.H., Kim, S.-B., Magagi, R., The soil moisture active passive validation experiment 2012 (SMAPVEX12): prelaunch calibration and validation of the SMAP soil moisture algorithms. IEEE Trans. Geosci. Remote Sens. 53 (2015), 2784–2801.
28. McNairn, H., Shang, J., Jiao, X., Champagne, C., The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification. IEEE Trans. Geosci. Remote Sens. 47 (2009), 3981–3992.
29. Schölkopf, B., Smola, A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. 2002, MIT press.
30. Serpico, S.B., Moser, G., Cattoni, A.F., Feature reduction for classification purpose. Hyperspectral Data Exploitation: Theory and Applications, 2007, 245–274.
31. Sinclair, G., The transmission and reception of elliptically polarized waves. Proc. IRE 38 (1950), 148–151.
32. Skriver, H., Svendsen, M.T., Thomsen, A.G., Multitemporal C-and L-band polarimetric signatures of crops. IEEE Trans. Geosci. Remote Sens. 37 (1999), 2413–2429.
33. Soria-Ruiz, J., McNairm, H., Fernandez-Ordonez, Y., Bugden-Storie, J., Corn monitoring and crop yield using optical and RADARSAT-2 images. Geoscience and Remote Sensing Symposium, 2007, IGARSS 2007. IEEE International. IEEE, 2007, 3655–3658.
34. Tanase, M.A., Panciera, R., Lowell, K., Tian, S., Hacker, J.M., Walker, J.P., Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests. Remote Sens. Environ. 145 (2014), 93–104.
35. Van Zyl, J.J., Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 27 (1989), 36–45.
36. Yamaguchi, Y., Moriyama, T., Ishido, M., Yamada, H., Four-component scattering model for polarimetric SAR image decomposition. IEEE Trans. Geosci. Remote Sens. 43 (2005), 1699–1706.
37. Yekkehkhany, B., Homayouni, S., McNairn, H., Safari, A., Multi-temporal full polarimetry L-band SAR data classification for agriculture land cover mapping. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International. IEEE, 2014, 2770–2773.
38. Yu, J., Yan, Q., Zhang, Z., Ke, H., Zhao, Z., Wang, W., Unsupervised classification of polarimetric synthetic aperture radar images using kernel fuzzy C-means clustering. Int. J. Image Data Fusion 3 (2012), 319–332.
39. Zhang, L., Zhang, J., Zou, B., Zhang, Y., Comparison of methods for target detection and applications using polarimetric SAR image. Piers Online 4 (2008), 140–145.