COMPLEX STRUCTURAL INFORMATION EXTRACTION BY APPLYING SYNERGISTIC PROCESSING TECHNIQUES TO ASTER IMAGES: PRINCIPAL COMPONENT ANALYSIS (PCA), FAST FOURIER TRANSFORM (FFT), AND REDUNDANT WAVELET TRANSFORM (RWT) --- WITH EXAMPLES FROM THE NEOPROTEROZOIC ALLAQI SUTURE, SOUTHEASTERN EGYPT
Satellite remote sensing data are usually used to analyze the spatial distribution pattern of the geological structures. However, in any band(s) of a satellite sensor, both structures or structure-related rocks and their surroundings are mixed and illustrated simultaneously. Separation of the structure-related rocks and structures from their surroundings simplifies this complicated problem. In this paper, the Advanced Space-borne Thermal Emission and Reflectance Radiometer (ASTER) data covering the Allaqi Suture are utilized to fulfill this separation. Based on the three visible and near infrared (VNIR) and six short wave infrared (SWIR) bands, the principal component analysis (PCA) is performed by using the 9 X 9 covariance matrix generated from the nine selected ASTER bands. In this case, the PCA band 5 is selected because the ophiolites and the ophiolite-related structures are included while their surroundings are suppressed. Furthermore, the forward fast fourier transform (FFT) and inverse FFT are used to reduce the noises generated during the PCA calculation mentioned above and the mosaicking processes of the ASTER scenes. In the end, the contrast between the structural lineaments and their surroundings are enhanced by applying the threshold redundant wavelet transform (RWT).
The final image after performing the synergistic processing techniques mentioned above is used for the detailed structural interpretation. This interpretation together with the field structural data sets provides the basic frame of a tentatively proposed evolution model for the Allaqi Suture. This model is compared with the ones proposed in the published literatures and proved more reasonable.