Improving the Accuracy of Land Cover Classification Using Fusion of Polarimetric SAR and Hyperspectral Images

Journal of the Indian Society of Remote Sensing

Volumn 44, Issue 6, 2016, Pages 1017-1024

  Shokrollahi, Mahin ^a   Ebadi, Hamid ^a  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Classification; Decision level fusion; Feature level fusion; Hyperspectral images; PolSAR data

Indexed keywords

ACCURACY ASSESSMENT; DECISION SUPPORT SYSTEM; IMAGE CLASSIFICATION; LAND COVER; MULTISPECTRAL IMAGE; PERFORMANCE ASSESSMENT; SATELLITE DATA; SYNTHETIC APERTURE RADAR;

EID: 84959370869     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-016-0559-4     Document Type: Article

References (27)

1. Al-Wassai, F. A., Kalyankar, N., & Al-Zaky, A. A. (2011). Multisensor images fusion based on feature-level. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), I(V), 124–138.
2. Baassou B., He, M., Farid, M. I., & Mei, S. (2013). Hyperspectral image classification based on iterative Support Vector Machine by integrating spatial-spectral information. In: IEEE International Geoscience and Remote Sensing Symposium, 1023–1026.
3. Benediktsson, J. A., & Kanellopoulos, I. (1999). Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 37, 1367–1377.
4. Borghys, D., Shimoni, M., Degueldre, G., & Perneel, C. (2007). Improved object recognition by fusion of hyperspectral and SAR data. In: 5th EARSeL SIG IS workshop on Imaging Spectroscopy:“ Innovation in environmental research”.
5. Chang, Y.-L., Chen, C.-T., Han, C.-C., Fan, K.-C., Chen, K., & Chang, J.-H. (2003). Hyperspectral and sar imagery data fusion with positive boolean function. In: AeroSense, 765–776.
6. Cloude, S. R., & Pottier, E. (1996). A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34, 498–518.
7. Feng, Q., Chen, E.-X., Li, Z., Guo, Y., Zhou, W., Li, W., & Xu, G. (2012). Land cover classification by Support Vector Machines using multi-temporal polarimetric SAR data. In: IEEE International Geoscience and Remote Sensing Symposium, 6244–6246.
8. Fröhlich, H., Chapelle, O., & Schölkopf, B. (2003). Feature selection for support vector machines by means of genetic algorithm. In: IEEE International Tools with Artificial Intelligence, 142–148.
9. Fukuda S., Hirosawa, H. (2001). Support vector machine classification of land cover: application to polarimetric SAR data. In: IEEE International Geoscience and Remote Sensing Symposium, 187–189.
10. Ghanbari, Z., & Sahebi, M. R. (2014). Improved IHS algorithm for fusing high resolution satellite images of urban areas. Journal of the Indian Society of Remote Sensing, 42, 689–699.
11. Jafari, M., Maghsoudi, Y., Zoej, V., & Javad, M. (2015). A new method for land cover characterization and classification of polarimetric SAR data using polarimetric signatures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3595–3607.
12. Jouan, A., & Allard, Y. (2004). Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery. Information Fusion, 5, 251–267.
13. Karimi, Y., Prasher, S., Patel, R., & Kim, S. (2006). Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture, 51, 99–109.
14. Koetz, B., Morsdorf, F., Van der Linden, S., Curt, T., & Allgöwer, B. (2008). Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. Forest Ecology and Management, 256, 263–271.
15. Lee, J.-S., Grunes, M. R., & Kwok, R. (1994). Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. International Journal of Remote Sensing, 15, 2299–2311.
16. Maghsoudi, Y., Collins, M., & Leckie, D. G. (2012). Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers. International Journal of Applied Earth Observation and Geoinformation, 19, 139–150.
17. Pal, M., & Mather, P. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 1007–1011.
18. Petropoulos, G. P., Arvanitis, K., & Sigrimis, N. (2012). Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert Systems with Applications, 39, 3800–3809.
19. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823–854.
20. Salehi, M., Sahebi, M. R., & Maghsoudi, Y. (2014). 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, 1394–1401.
21. Scholköpf, B., & Smola, A. (2002). Support vector machines, regularization, optimization, and beyond. Cambridge: MIT press.
22. Solberg, A. H. S., Jain, A. K., & Taxt, T. (1994). Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 32, 768–778.
23. Tang, K.-S., Man, K.-F., Kwong, S., & He, Q. (1996). Genetic algorithms and their applications. IEEE Signal Processing Magazine, 13, 22–37.
24. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988–999.
25. Waske, B., & Benediktsson, J. A. (2007). Fusion of support vector machines for classification of multisensor data. IEEE Transactions on Geoscience and Remote Sensing, 45, 3858–3866.
26. Yin, H., & Jian, C. (2012). Classification based on Four-component decomposition and SVM for PolSAR images. International Journal of Remote Sensing, 7, 1401–1406.
27. Zhang, J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1, 5–24.