Multiple SAR data integration using wavelet transform

The 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. The integration technique using the wavelet transform as proposed in the present paper utilizes the property of the wavelet transform that can decompose an image into an approximated image (low-frequencies) characterizing the spatially large and relatively distinct structures, and a detailed image (high-frequencies) in which the information on small detailed structures is 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