LAUSR

Hyperspectral remote sensing consolidates imaging and spectroscopy in a solitary system which frequently comprises big datasets and necessitates the novel processing methods. In the present study, Cheranmadevi Block of Tirunelveli District in Tamil Nadu is selected to extract the abundant limestone mineral. Hyperion is one of the freely available hyperspectral imagery containing 242 spectral bands with 10-nm intervals in the wavelength between 400 and 2500 nm. The main objectives of the present research work are to enhance the imagery visualization, end member extraction, and classification, and estimate the abundant limestone quantity by removing the striping error in Hyperion imagery. The scanning electron microscope with energy-dispersive X-ray spectroscopy analysis is performed to identify the chemical composition of limestone mineral. The spectral reflectance of limestone is characterized using analytical spectral devices like a field spectroradiometer. Limestone has deep absorption in the short-wave infrared region (1900–2500 nm) around 2320–2340 nm due to their calcite composition (CaCO3). The feature extraction in Hyperion data is performed using various preprocessing steps like bad bands removal, vertical strip removal, and radiance and reflectance creation. To improve the classification accuracy, vertical strip removal process is performed using a local destriping algorithm. The absolute reflectance is achieved by the atmospheric correction module using Fast Line-of-sight Atmospheric Analysis of Hypercubes. The acquired reflectance image spectra are compared with the spectral libraries of USGS, JPL, and field spectra. Destriping enhances qualities of Hyperion data interims of the spectral profile, radiance, reflectance, and data reduction methods. The present research work focused on the local destriping algorithm to increase the quality and quantity of limestone deposit extraction.