Gamma-ray protection capacity evaluation and satellite data based mapping for the limestone, charnockite, and gneiss rocks in the Sirugudi taluk of the Dindigul district, India

Landsat-8 OLI/SRTM and gravity characteristics of the Pan-African fracture aquifers of the north central Cameroon region (central Africa)

“…According to Refs. [ [11] , [12] , [13] , 57 ], CB is a mathematical operation applied to bands. According to Ref.…”

“…4 a-d). The PCA technique has been utilized internationally [ [11] , [12] , [13] , [60] , [61] , [62] , [63] , [64] ] and in Cameroon [ [29] , [30] , [31] , 65 ]. Next, the color composition (CC) technique assigns red (R), green (G), and blue (B) to the initial image to optimize visual analysis.…”

“…[ 9 , 10 ], the major water flow comes from deep fractures. Recently, remote sensing studies provided new insights on mineral resource mapping [ [11] , [12] , [13] , [14] , [15] , [16] , [17] ] and on spatial mapping of this reservoir [ [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] ]. Remote sensing is advantageous where plant cover and soil cover are important.…”

Impact of DEMs for Improvement Sentinel 2 Lithological Mapping Utilizing Support Vector Machine: A Case Study of Mineralized Fe-Ti-Rich Gabbroic Rocks from the South Eastern Desert of Egypt

“…Consequently, several image processing techniques, including False Color composites (FCCs), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), besides a previous geological map [42], were integrated to discriminate between the complicated lithologies of the study area. FCC is a normal RGB representation of 3 bands (any combination except the true-color composite) that depends mainly on the ability of each band to enhance lithological features, based on their spectral coverage [11,[43][44][45][46]. Combining representative bands can greatly improve the identification of features.…”

“…PCA is a dimensionality-reduction statistical method used to obtain information about features aiming to better highlight certain targets [47]. This could be achieved by converting correlated variables to uncorrelated variables through an orthogonal transformation known as principal component analysis [43,44]. However, although PCA significantly helps to reduce the dimensions of satellite data, it mostly retains the important information of the images.…”