High End Multi-Sensor Remote Sensing Integration and Its Application in Precision Mineral Exploration

Abstract

The objective of this research was to determine the degree of efficiency of the enhanced multi-sensor remote sensing integration in the context of the precision mineral search. The research focused on the issue of how to increase the accuracy of mineral detection and at the same time decrease cost and bearing on the nature. This paper utilized remote sensing data analysis with additional qualitative geoscientific interpretation in an integrated manner based on both quantitative and qualitative research design. Multispectral images from Landsat 8, Sentinel-2, hyperspectral from AVIRIS, HyMap, SAR from Sentinel-1 and LiDAR were fused using machine learning too including Convolutional Neural Networks and Random Forests. Sampling was done in the field with two field portable X-ray fluorescence spectrometers, and several field spectroradiometers. Results showed a 17. Higher true positive detection rates of mineral deposits by 5% as compared to the single sensor approaches. This combined approach indicated 30 per cent more potential exploration targets compared to the traditional approach while it reduced the preliminary field costs by forty-five per cent. Multi temporal image analysis with chronological sequence display showed minute signs of mineralization in desert country. Surveys regarding the environmental effects proved that First Nations incurred only 40% of the impact that might be caused by conventional exploration strategies. A great extent of economic analysis shown that large-scale surveys could enhance the return of investment up to 32%. Nevertheless, constraints were noted when the thickness of the overburden was huge. Based on the findings of this study, the proposed IMS RS is found to improve mineral exploration productivity and reliability and is a more sustainable model in the identification of resources. Some suggestions are to use this technology in most exploration phases and further study of the way on how to do the sensor fusion.