Unearthing the Lonar Crater Using Hyperspectral Remote Sensing and Validating Through Non-destructive Approach

DOI

10.2991/978-94-6463-136-4_66How to use a DOI?

Keywords

Minerals; Hyperspectral; Remote Sensing; Support Vector Machine

Abstract

Remote Sensing is a prominent technology, applied at all fields of earth science. It is useful in the identification and mapping of minerals, vegetation, different materials and backgrounds by capturing the electromagnetic energy reflected from them. The analysis of remote places or structures is possible with the help of remote sensing with economic and timely manner. So, the proposed work focuses on exploring the Lonar crater with the help of remote sensing. The earth surface is enriched with number of useful minerals. Minerals are very important resources contributing in the wealth of a country. So, this work focused on mineral mapping and mineral identification at Lonar Crater by using high resolution hyperspectral images acquired by Hyperion sensor. The validation of minerals identified with hyperspectral imaging was completed by the use of nondestructive approach of spectroradiometer.

The various minerals are identified by imaging analysis and validated through non-destructive approach. The significant results of the proposed methodology are: i) the type of rock at the Lonar crater is identified as basaltic igneous rocks, ii) the origin of Lonar crater is due to the extrusive volcanic activity and iii) The Lonar lake is saline in nature. The Spectral Classification using SAM gave accuracy of 89.01% with 0.8753 kappa coefficient and SVM technique, gave the accuracy of 97.25% with 0.9688 kappa coefficient indicating more match with the ground truth.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ranjana Gore
AU  - Abhilasha Mishra
AU  - Ratnadeep Deshmukh
AU  - Dipa Dharmadhikari
PY  - 2023
DA  - 2023/05/01
TI  - Unearthing the Lonar Crater Using Hyperspectral Remote Sensing and Validating Through Non-destructive Approach
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 761
EP  - 773
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_66
DO  - 10.2991/978-94-6463-136-4_66
ID  - Gore2023
ER  -