Validating (MNF) transform to determine the least inherent dimensionality of ASTER image data of some uranium localities at Central Eastern Desert, Egypt

Abstract

One of the main determinations of geological remote sensing is to develop and improve different image processing algorithms and methods for mineral mapping and rock type discrimination. This paper investigates one of those sophisticated algorithms; the validity of Minimum Noise Fraction (MNF) components to improve the spectral separability of The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor image data of Terra satellite, and to develop a standard method of noise removal and image enhancement. Previous studies that used in image processing to eliminate MNF eigenvalues, eliminate values less than the unit. However, removing MNF eigenvalues more than this value and its effect on the data, has not been examined. Therefore, in this paper, we demonstrate that we can eliminate more than the unit up to 2 eigenvalue without a negative inherent effect on the origin data structure. A comparison study using different Image Quality Measures (IQMs) methods, has been accomplished on data distribution before and after MNF transformation of the original and atmospherically corrected data. Matched Filter (MF) classification was performed on data before and after MNF to map a chosen known alteration mineral (Kaolinite) by matching the image endmember spectra and its spectra from USGS library. By examining the spectral trends of specific mineral alteration type across these transformed channels, areas of interest that are unique by the related previous well-known uranium mineralization may become superficial apparent. This examination also showed how the use of MNF as a pre-processing technique could improve the capability to extract information from ASTER data of El-Missikat, El-Eridya and Kab Amiri areas in the Central Eastern desert as a case study district for uranium mineralization occurrences.