Mapping of Mineral Zones using the Spectral Feature Fitting Method in
International Research Journal of Engineering and Technology (IRJET)
The main aim of this paper is to describe the Spectral Feature Fitting (SFF) Algorithm for the mineral mapping. Hyperspectral satellite imagery is having a high resolution which is more useful for the mineral identification and mapping and hence, capable to replacing the traditional techniques such as field-based approach and multispectral remote sensing for the mineral identification and classification. Pixels of the hyperspectral imagery contains the unique information about the materials
... t the materials present on the surface as it is having a high spectral resolution as well. Airborne hyperspectral imagery is having very high spectral as well as spatial resolution as compared to spaceborne hyperspectral imagery. Jahazpur belt area is in the southern part of the Jahazpur village of Bhilwara in Rajasthan. SFF has been applied to process Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) imagery for identification and enhancement of the mineral mapping process with better accuracy. Minimum Noise Fraction (MNF) algorithm is used for the reduction of the dimensionality of the data. Pixel Purity Index (PPI) and n-Dimensional visualization for the extraction of the pure pixels (endmembers) from the cluster of pixels. That information is used for the classification with the help of the SFF algorithm. SFF method helps in the processing of imagery with high efficiency and preparing the mineral distribution map of the study area.