Comparative analysis of mineral mapping for hyperspectral and multispectral imagery

Abstract

The traditional approaches of mineral-mapping are time consuming and expensive process. Remote sensing is a tool to map the minerals precisely using their physical, chemical and optical properties. In the present study, Tirunelveli district in Tamil Nadu is selected to extract the abundant mineral such as Limestone using Hyperion and Landsat-8 OLI imageries. The chemical composition of the mineral is identified using scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis. The spectral reflectance of minerals is characterized using analytical spectral device (ASD) field spectroradiometer. The minerals showed deep absorption in short wave infrared region from 1800 to 2500 nm. The mineral mapping in hyperspectral data is performed using various preliminary processing such as bad band removal, vertical strip removal, radiance and reflectance generation and postprocessing steps such as data dimensional reduction, endmember extraction and classification. To improve the classification accuracy, the vertical strip removal process is performed using a local destriping algorithm. Absolute reflectance of Hyperion and Landsat-8 OLI (Operational Land Imager) imageries is carried out using the FLAASH (fast line-of-sight atmospheric analysis of hypercubes) module. Spectral data reduction techniques in reflectance bands performed using minimum noise fraction method. The noiseless reflectance bands spatial data reduced by the Pixel Purity Index method in the threshold limit of 2.5 under 10,000 repetitions. The obtained reflectance imagery spectra compared with the spectral libraries such as USGS (United States Geological Survey), JPL (Jet Propulsion Laboratory) and field spectra. Endmembers of minerals are carried out using high probability score obtained from the various methods such as SAM (spectral angle mapper), SFF (spectral feature fitting) and BE (binary encoding). The mineral mapping of both imageries is carried out using a supervised classification approach. The results showed that hyperspectral remote sensing performed good results as compared to multispectral data.