5. CONCLUSIONS 
In this paper, the integration of FS using GA method and the 
MCS techniques with DS algorithm has been proposed and 
implemented for classifying different combined datasets of 
multi-date ENVISAT/ASAR, ALOS/PALSAR and Landsat 5 
TM- images. Results of classification revealed that the 
proposed (FS-GA-DS model) method outperform both classical 
non-FS and FS-GA method. The FS-GA-DS model always gave 
significantly higher accuracy than any single best classifier. The 
FS-GA-DS method produced the highest overall classification 
accuracy of 88.29% for the largest combined dataset consisted 
of original SAR and optical images, their textural information 
and NDVI. The FS-GA method is also an effective method for 
classification of multisource remote sensing data, though it is 
less accurate than the FS-GA-DS method. Finally, the 
classification results confirm the advantages of multisource 
remote sensing data, particularly the integration of SAR and 
optical data, for land cover mapping. The combination approach 
often performed better than any single typed dataset. 
6. REFERENCES 
Benediktsson, J.A., Chanussot, J. and Fauvel, M., 2007. 
Multiple Classifier Systems in Remote Sensing: From Basics to 
Recent Developments: MCS 2007, LNCS 4472, Springer 
Verlag, Berlin 2007, pp. 501-512. 
Bruzzone, L., Marconcini, M., Wegmuller, U., Wiesmann, A., 
2004. An advanced system for the automatic classification of 
multitemporal SAR images. [EEE transaction on Geoscience 
and remote sensing, 42(6), pp. 1321 — 1334. 
Chu, H.T., and Ge, L., 2010. Land cover classification using 
combination of L- and C-band SAR and optical satellite images. 
In: 31” Asian conference of Remote Sensing (ACRS), Hanoi, 1-5 
November. 
Dixon, B., and Candade, N., 2008. Multispectral land use 
classification using neural networks and support vector 
machines: one or the other, or both. International Journal of 
Remote Sensing, 29(4), pp. 1185-1206. 
Du, P., Zhang, W., and Sun, H., 2009. Multiple Classifier 
Combination for Hyperspetral Remote Sensing Image 
Classification. MCS 2009, LNCS 5519, Springer-Verlag Berlin 
Heidelberg. pp.52-61. 
Erasmi, S., and Twele, A., 2009. Regional land over mapping in 
the humid tropics using combined optical and SAR satellite data 
— a case study from Central Sulawesi, Indonesia. International 
Journal of Remote Sensing, 30(10), pp. 2465-2478. 
Huang, C.L. and Wang, C.J, 2006. A GA-based feature 
selection and parameters optimization for support vector 
machines. Expert Systems with Applications, 31, 231-240. 
Kavzoglu, T., and Mather, P. M., 2003. The use of 
backpropagating artificial neural network in land cover 
classification. International journal of Remote sensing, 24(23), 
pp. 4907-4938. 
Kavzoglu, T., and Colkesen, L, 2009. A kernel functions 
analysis for support vector machines for land cover 
classification, International journal of Applied Erath 
Observation and Geoinformation, 11, pp: 352 — 359. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
Lee, S., and Lathrop, R. G., 2006. Subpixel analysis of Landsat 
ETM- using Self-Organizing Map (SOM) neural networks for 
urban land cover characterization. IEEE Transactions on 
Geoscience and Remote Sensing, 44(6), pp. 1642-1654. 
Lu, D., and Weng, Q., 2007. A survey of image classification 
methods and techniques for improving classification 
performance. International Journal of Remote Sensing, 44(5), 
pp: 823-870. 
Salah, M., Trinder, J. and Shaker, A., 2009. Evaluation of the 
self-organizing map classifier for building detection from lidar 
data and multispectral aerial images. Journal of Spatial 
Science, 54, pp. 15-34. 
Salah, M., Trinder, J., Shaker, A., Hamed, M., and Elsagheer, 
A., 2010. Intergrating Multiple Classifiers with Fuzzy Majority 
Voting for Improveed Land cover Classifiation. In: /APRS, Vol. 
XXXVIII, Part 3A — Saint-Mandé, France, September 1-3. 
Sheoran, A., Haack, B., and Sawaya, S., 2009. Land cover/use 
classification using optial and quad polarization radar imagery. 
ASPRS 2009 Annual Conferencce, Baltimore, Maryland, USA, 
9 — 13 March. 
Soria-Ruiz, J., Fernadez-Ordonez, Y., and Woodhouse, I. H., 
2010. Land-cover lassification using radar and optical images: a 
case study in Central Mexico. International Journal of Remote 
Sensing, 31(12), pp. 3291-3305. 
Trinder, J. C., and Salah, M., 2010. Combining statistical and 
neural classifiers using Dempster-Shafer theory of evidence for 
improved building detection. In: Proceeding of 1 3 
Australasian Remote Sensing & Photogrammetry Conference 
(15^ ARSPC), Alice Springs, Australia, 13-17 September. 
Tso, B., and Mather, P., 2009. Classification Methods for 
remotely sensed data, Second edition, CRC Press, Taylo & 
Francis Group. 
Waske, B., and Benediktsson, J.A., 2007. Fusion of Support 
Vector Machines for classification of multisensor data, /EEE 
Transactions on Geosciences and Remote Sensing, 45(12) pp. 
4842-4845. 
Zhou, M., Shu, J. and Chen, Z., 2010. Classification of 
hyperspetral remote sensing image based on Genetic Algorithm 
and SVM. Remote Sensing & Modeling of Ecosystems for 
Sustainability VII, Proc. of SPIE Vol.7809, 78090A, 
doi:10.1117/12.860153. 
7. ACKNOWLEDGEMENT 
ALOS/PALSAR Level 1.1 products were processed by 
ERSDAC, Japan. Copyright of raw data belongs to METI and 
JAXA. 
ENVISAT/ASAR were kindly provided by the European Space 
Agency (ESA). 
Landsat 5 TM Level 1T products were processed and provided 
by the USGS Earth Resources Observation and Science Center.