Although remote sensing has been widely used in geological investigations, the lithological classification of the area interested, based on medium-spatial and spectral resolution satellite data, is often not successful because of the complicated geological situation and other factors like inadequate methodology applied and wrong geological models.
The study area of the present thesis is located in southwest of the Prieska sub-basin, Transvaal Supergroup, South Africa. This area includes mainly Neoarchean and Proterozoic sedimentary rocks partly uncomfortably covered by uppermost Paleozoic and lower Mesozoic rocks and Tertiary to recent soils and sands. The Precambrian rocks include various formations of volcanic and intrusive rocks, quartzites, shales, platform carbonates and Banded Iron Formations (BIF). The younger rocks and soils include dikes and shales, glacial sedimentary rocks, coarser siliciclastic rocks, calcretes, aeolian and fluvial sands, etc. Prospect activity for mineral deposits necessitates the detailed geological map (1:100000) of the area.
In this research, a new rule-based classification system (RBS) was put forward, integrating spectral characteristics, textural features and ancillary data, such as general geological map (1:250000) and elevation data, in order to improve the lithological classification accuracy and the subsequent mapping accuracy in the study area. The proposed technique was mainly based on Landsat TM data and ASTER data with medium resolution. As ancillary data sets, topographic maps and general geological map were also available. Software like ERDAS©, Matlab©, and ArcGIS© supported the procedures of classification and mapping.
The newly developed classification technique was performed by three steps. Firstly, the geographic and atmospheric correction was performed on the original TM and ASTER data, following the principal component analysis (PCA) and band ratioing, to enhance the images and to obtain data sets like principal components (PCs) and ratio bands. Traditional maximum-likelihood supervised classification (MLC) was performed individually on enhanced multispectral image and principal components image (PCs-image). For TM data, the classification accuracy based on PCs-image was higher than that based on multispectral image. For ASTER data, the classification accuracy of PCs- image was close to but lower, than that of multispectral image. As one of the encountered Banded Iron Formations, the Griquatown Banded Iron Formation (G-BIF) was recognized well in TM-principal components image (PCs-image).
In the second step, textural features of different lithological types based on TM data were analyzed. Grey level co-occurrence matrix (GLCM) based textural features were computed individually from band 5 and the first principal component (PC1) of TM data. Geostatistics-based textural features were computed individually from the 6 TM multispectral bands and 3 principal components (PC1, PC2 and PC3). These two kinds of textural features were individually stacked as extra layers together with the original 6 multispectral bands and the 6 principal components to form several new data sets. Ratio bands were also individually stacked as extra layers with 6 multispectral bands and 6 principal components, to form other new data sets. In the same way new data sets were formed based on ASTER data. Then, all of the new data sets were individually classified using a maximum likelihood supervised classification (MLC), to produce several classified thematic images. The classification accuracy based on the new data sets are higher than that solely based on the spectral characteristics of original TM and ASTER data. It should be noticed that for one specific rock type, the class value in all classified images should correspond to its identified (ID) value in digital geological map.
The third step was to perform the rule-based system (RBS) classification. In the first part of the RBS, two classified imag
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