Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM Method

Roy, S and Bhattacharya, S and Omkar, SN (2022) Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM Method. In: PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science .

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Abstract

High-resolution hyperspectral remote sensing can provide a large-scale mapping of pure spectra along with perturbed/mixed spectra of minerals within a scene. Among the high-computational �per-pixel� methods, machine learning is a well-known automated technique to data science, being most flexible to map new spectra or perturbed/mixed spectra of minerals as an individual category. Since limited mineral samples often partly represent the complex mineralogy of a large site, a distributed mapping requires to be conducted using a scalable method that works even with a smaller number of training samples. In this regard, we introduce an integrated extreme learning machine (IELM) method that maps qualitatively the pure spectra and perturbed/mixed spectra of every surface type. This mapping has been further integrated into a quantitative analysis of the perturbation/mixing nature of pure spectra. The large-scale mapping of the Jahazpur mineralised belt has been conducted by a MapReduce model with the IELM method using AVIRIS-NG (Airborne Visible-Infrared Imaging Spectrometer-Next Generation) observation. In the validation process, the IELM method achieves 98.08 accuracy with high signal-to-noise (SNR) valued AVIRIS-NG data and 96.54 with low-SNR synthetic data in the presence of 269 training samples. The IELM method shows better efficacy than a spectral feature fitting approach in assessment. The analyses of perturbed and mixed spectra implicate that an additive spectral variability model and linear mixing model fit for the present data of our investigation. These analytical findings can be further extended for a �sub-pixel� method (e.g. spectral unmixing) to reach an application like lithology or host-rock mapping. © 2022, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V.

Item Type:	Journal Article
Publication:	PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	14 Feb 2022 15:38
Last Modified:	14 Feb 2022 15:38
URI:	http://eprints.iisc.ac.in/id/eprint/71343

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