Fusion and Classification of Synthetic Aparture Radar and Multispectral Sattellite Data

Abstract

In this study, synthetic aperture radar (SAR) and multispectral data are fused with different methods in order to observe the effect of fusion methods on the accuracy of different classification techniques. At the same time, different polarizations of SAR data are included in fusion process and results are examined. The fusion methods that are used in this study are Brovey Color Normalized, Hue Saturation Value (HSV), Gram Schmidt (GS) Spectral Sharpening and Principal Components (PC) Spectral Sharpening. Fused images are classified using k-nearest neighbor, support vector machine and radial based function neural network The study area is chosen on Menemen Plain, which contains agricultural lands, and it is located in Izmir. Multispectral Rapid Eye satellite image and TerraSAR-X radar data are used for the analysis. Achieved results were presented in the tables. The highest accuracy is achieved by K-NN classification of TerraSAR-X and VH fusion with GS method as 95.74%