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A Fully-Automated Framework for Mineral Identification on Martian Surface Using Supervised Learning Models

  • Published:2023 Issue: Volume:11 Page:13121-13137
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Kumari Priyanka1ORCID,Soor Sampriti2ORCID,Shetty Amba1ORCID,Koolagudi Shashidhar G.3

Affiliation:

1. Department of Water Resources and Ocean Engineering, NIT Karnataka, Surathkal, India

2. Systems Science and Informatics Unit, Indian Statistical Institute, Bengaluru, India

3. Department of Computer Science and Engineering, NIT Karnataka, Surathkal, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface;The Egyptian Journal of Remote Sensing and Space Sciences;2024-09

2. Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

4. Spectroscopy of Ammonium Occupying Symmetry-Inappropriate Positions in Crystal Structures of Salts;Journal of Applied Spectroscopy;2024-07

5. Mineral classification on Martian surface using CRISM hyperspectral data: a survey;Journal of Applied Remote Sensing;2023-11-11
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