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

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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.

Zusammenfassung

Automatisierte großmaßstäbliche Kartierung des Jahazpur Mineraliengürtels durch ein MapReduce-Modell mit integrierter ELM-Methode. Die hochauflösende hyperspektrale Fernerkundung kann eine großmaßstäbliche Kartierung von reinen Spektren zusammen mit gestörten/gemischten Spektren von Mineralien innerhalb einer Szene liefern. Unter den "Pro-Pixel"-Methoden ist das maschinelle Lernen eine bekannte automatisierte Technik für die Datenwissenschaft, die am flexibelsten ist, um neue Spektren oder gestörte/gemischte Spektren von Mineralien als eine individuelle Kategorie zu kartieren. Da begrenzte Mineralproben oft nur teilweise die komplexe Mineralogie eines großen Standorts repräsentieren, muss eine verteilte Kartierung mit einer skalierbaren Methode durchgeführt werden, die auch mit einer kleineren Anzahl von Trainingsproben funktioniert. In diesem Zusammenhang stellen wir ein integriertes maschinelles Extrem-Lernverfahren (IELM) vor, das die reinen Spektren und die gestörten/gemischten Spektren jedes Oberflächentyps qualitativ kartiert. Diese Kartierung wurde in eine quantitative Analyse der Störungs-/Vermischungseigenschaften der reinen Spektren integriert. Die großmaßstäblicher Kartierung des mineralisierten Gürtels von Jahazpur wurde mit einem MapReduce-Modell mit der IELM-Methode unter Verwendung von AVIRIS-NG (Airborne Visible-Infrared Imaging Spectrometer-Next Generation) durchgeführt. Bei der Validierung erreicht die IELM-Methode eine Genauigkeit von 98,08 % bei AVIRIS-NG-Daten mit hohem Signal-Rausch-Verhältnis (SNR) und 96,54 % bei synthetischen Daten mit niedrigem SNR in Anwesenheit von 269 Trainingsproben. Die IELM-Methode zeigt bei der Bewertung eine bessere Wirksamkeit als ein Ansatz zur Anpassung der Spektralmerkmale. Die Analysen der gestörten und gemischten Spektren deuten darauf hin, dass ein additives spektrales Variabilitätsmodell und ein lineares Mischungsmodell für die vorliegenden Daten unserer Untersuchung geeignet sind. Diese analytischen Ergebnisse können für eine "Sub-Pixel"-Methode (z. B. spektrales Unmixing) weiter ausgebaut werden, um eine Anwendung wie Lithologie- oder Wirtsgesteinskartierung zu erreichen.

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Computer Code Availability

The algorithmic code includes the proposed integrated ELM method that have been implemented in platform of eclipse Java EE developer. The demo data are selected from the study area itself with dimension of 200 pixels \(\times\) 200 pixels. The implemented code is developed by Sukanta Roy (https://github.com/roysukanta/MineralMapping).

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Acknowledgements

We would like to thank the whole team of AVIRIS-NG Science Campaign of SAC, Ahmedabad, ISRO, India for sharing their expertise and field knowledge in our study. We also would like to thank the anonymous two reviewers for their incisive comments and suggestions.

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Authors and Affiliations

  1. Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    Sukanta Roy

  2. Space Applications Centre, Indian Space Research Organization, Ahmedabad, India

    Satadru Bhattacharya

  3. Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    Subbaramajois Narasipur Omkar

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  1. Sukanta Roy
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  2. Satadru Bhattacharya
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  3. Subbaramajois Narasipur Omkar
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Contributions

SR conceived of the presented idea to develop the automation methodology and performed the data analysis. SB provided the authentic data of remote sensing and contributed the knowledge of in situ investigation to validate the science from the findings of our study. SNO supervised the flow of our presented work. All the authors discussed to finalise the manuscript.

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Correspondence to Sukanta Roy.

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Roy, S., Bhattacharya, S. & Omkar, S.N. Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM Method. PFG 90, 191–209 (2022). https://doi.org/10.1007/s41064-021-00188-3

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