Identification of Rocks and Their Quartz Content in Gua Musang Goldfield Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery

Abstract

Quartz is an important mineral element and the most abundant rock-forming mineral that controls the mineralogy of a reservoir. At the surface, quartz is more stable than most other rock minerals because it is made up of interlocking silica that makes it quite resistant to mechanical weathering. Quartz abundance is an indication of mineralization in many metal deposits; therefore, identification and mapping of quartz in rocks are of great value for exploration and resource potential assessments. In this study, thermal infrared (TIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery were used to identify quartz contained rocks in Gua Musang. First, the image was corrected for atmospheric effect and the study area subset for further processing. Thereafter, spectral transformation (principal component analysis (PCA)) was implemented on the TIR bands and the resulting principal component (PC) images were analysed. The three optimal PCs were selected using the strength of spectral interaction and the eigenvalues of each band. To discriminate between quartz-rich and quartz-poor rocks, RGB false colour composite and greyscale image of one of the PCs were analysed. The result shows that volcanogenic igneous rock and carbonate sedimentary rocks of Permian formation are quartz-poor while Triassic sedimentary rock made up of organic particles and sandstone is quartz-rich. On the contrary, the quartz content in the metamorphic rock varies across the area but is richer in quartz content than the igneous and carbonate rocks. Classification of the composite image classified using maximum likelihood (ML) supervised classification method produced overall accuracy and Kappa coefficient of 96.53%, and 0.95, respectively.

Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

“…It has been observed in Table 2 actually describe the LU/LC dataset into its own direction and as whole these features disclose the entire texture patterns. It has been discussed by many researchers [10][11][12][13][14] that five control features, that is, window size, texture derivative(s), input channel (i.e., spectral channel to measure the texture), quantization level of output channel (8 bits, 16 bits, and 32 bits), and the spatial components, that is, interpixel distance and angle during cooccurrence matrix computation, play a very important role during the analysis of texture features.…”

“…They proved quantitatively that fusion plays important role in better interoperational capabilities and reduces the ambiguity linked with the data acquired by different sensors or by same sensor with temporal variation. Quartz rich and poor mineral types are identified by using the image fusion method with the implementation of supervised classifier maximum likelihood (ML) and acquired overall accuracy and kappa coefficient of 96.53% and 0.95, respectively [14].…”

Band Ratios Matrix Transformation (BRMT): A Sedimentary Lithology Mapping Approach Using ASTER Satellite Sensor

“…Mapping sedimentary rock is a very challenging task by application of conventional image processing techniques to satellite remote sensing data in a variety of geological environments [ 8 , 9 , 20 , 45 , 46 ]. Lithological and mineralogical indices proposed by Ninomiya and Fu [ 18 ] and Ninomiya et al [ 12 ] for mapping sedimentary strata are threshold dependent and could not perform successfully in different geological regions.…”

A review of the past 5 years of international advances in multi- and hyperspectral satellite data application and processing techniques in geological research

“…Прогноз эпитермальных золотосеребряных месторождений на основании закономерностей, выделенных для известных месторождений, проведён в Аргентине (De Palomera et al, 2015) и в Иране (Yousefi et al, 2018a). В Китае по данным ASTER SWIR, TIR выполнено картирование горных пород и определение содержания в них кварца для поисков золотого оруденения (Yao et al, 2017). В Индии для прогноза зон, перспективных на золотосульфидную минерализацию, проведено комплексирование минералогических данных, полученных системой ASTER, магнитной, гравиметрической информации и данных наземной спектроскопии .…”