Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data-Reference-Cited by-同舟云学术

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Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data

Published:2024-07-07 Issue: Volume:27 Page:6168-6171
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Kumari Priyanka¹,Soor Sampriti²,Shetty Amba¹,Daya Sagar B. S.³

Affiliation:

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

2. Kalinga Institute of Industrial Technology,School of Computer Engineering,Bhubaneswar,India

3. Indian Statistical Institute,Systems Science and Informatics Unit,Bangalore,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642888.pdf?arnumber=10642888

Reference9 articles.

1. Partially-observed models for classifying minerals on Mars

2. The infinite mixture of infinite gaussian mixtures;Yerebakan;Advances in neural information processing systems,2014

3. A machine learning toolkit for CRISM image analysis

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

5. Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars

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