COMPARISON OF DIFFERENT FUSION ALGORITHMS IN URBAN AND AGRICULTURAL AREAS USING SAR (PALSAR AND RADARSAT) AND OPTICAL (SPOT) IMAGES

Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutionsand polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusionalgorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused imageswere performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as “urban” and “agricultural”. It has been observed that some of the methodshave enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers’ spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data