Multiple SAR data integration using wavelet transform

This paper investigates techniques of SAR image integration to reduce SAR look direction bias for geological application. Two approaches to SAR data integration; 1) the principal component analysis (PCA), and 2) the wavelet transform integration technique are investigated and tested. The test data include the CCRS's airborne C-band SAR data (HH-polarization) and the ERS-1 SAR data (VV-polarization) over the Sudbury basin, Ontario Canada. The PCA technique is very effective for integration of multiple sets of SAR image data. When only two data sets are available and correlation between them is very low, at least one more auxiliary data set is required. Integration technique using the wavelet transform as being proposed in this paper utilizes the property of the wavelet transform that can decompose an image into approximated image (low-frequencies) characterizing the spatially large and relatively distinct structures, and detailed image (high-frequencies) in which the information on small detailed structures are preserved. Test results show that enhancement of lineaments is comparable to the PCA approach. Fine detailed structures in the integrated image obtained using the wavelet transform are well retained compared to the PCA image.