Maqola

Application of hyperspectral and multispectral datasets for mineral mapping

D. RakhimovInformation-Analytical and Resources Center, Labzak street 1A, 100128 Tashkent, UzbekistanMukhiddin JulievTashkent Institute of Irrigation and Agricultural Mechanization Engineers, Tashkent, 100000, UzbekistanInobat AgzamovaTashkent State Technical University, University street 2, 100095 Tashkent, UzbekistanNasiba NormatovaTashkent State Technical University, University street 2, 100095 Tashkent, UzbekistanYa.S. ErmatovaTashkent State Technical University, University street 2, 100095 Tashkent, UzbekistanD. BegimkulovTashkent State Technical University, University street 2, 100095 Tashkent, UzbekistanLazizakhon GafurovaNational University of Uzbekistan, University street 4, 100174 Tashkent, UzbekistanM. HakimovaKarshi engineering economics institute, Mustakillik street 225, 180100 Karshi, UzbekistanOlimaxon ErgashevaNational University of Uzbekistan, University street 4, 100174 Tashkent, Uzbekistan

E3S Web of Conferencesjournal2023en

ABI

Annotatsiya

In this study, hyperspectral datasets are simulated from multispectral data using a spectral reconstruction approach which is a sensor-independent technique. This technique makes use of information from atmospherically corrected multispectral Remote Sensing (MRS) data and normalized ground spectra for the simulation of HRS data. In this study EO-1, the ALI dataset was used for the simulation of hyperspectral Remote Sensing (HRS) data to discover the Udaipur region’s unique minerals. A total of 61 spectral bands with 10 nm bandwidth were simulated. The simulated HRS data were validated using visual interpretation, statistical and classification approaches. Simulated HRS data from EO-1 Advanced Land Imager (ALI) has shown a high correlation with EO-1 Hyperion data. Spectral Angle Mapper (SAM) classification was also performed on simulated hyperspectral data for mineral mapping. It was observed that simulated hyperspectral data have shown comparable results with Hyperion and are better than their corresponding multispectral datasets.

Mavzular

Geochemistry and Geologic Mapping Remote-Sensing Image Classification Advanced Image Fusion Techniques

Identifikatorlar

DOI: 10.1051/e3sconf/202338604007

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