Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features

Sachin Lodhi; Sakshi Sakshi; Vinay Kukreja; Ankit Bansal

doi:10.1109/ICRITO56286.2022.9964662

Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features

Sachin Lodhi
, Sakshi Sakshi
, Vinay Kukreja
, Ankit Bansal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The red planet has persisted in being the top destination for space exploration and has also fascinated researchers to experiment with frequently launched its dataset of images. NASA has launched various missions on the track of space expeditions to explore the surface and the environment of the potential life-sustaining planet. Among all the missions for capturing images of the Martian terrain, the HiRISE experiment has been successful in coming up with the best datasets featuring the surface of Mars. In this research study, the authors have targeted the classification of this high-quality image dataset produced as the outcome of the HiRISE using a deep learning model. The proposed model is convoluted and built with the help of neural network layers (neuron layers). Based on the classification, the authors were able to classify the images of 7 terrain features of Mars, namely Crater, Impact Ejecta, Bright Dune, Dark Dune, Slope Streak, Spider, and Swiss Cheese. And the classification accuracy achieved by the model is 94.8%.

Original language	English
Title of host publication	2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781665474337
DOIs	https://doi.org/10.1109/ICRITO56286.2022.9964662
State	Published - 1 Jan 2022
Externally published	Yes
Event	10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022 - Noida, India Duration: 13 Oct 2022 → 14 Oct 2022

Publication series

Name	2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022

Conference

Conference	10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022
Country/Territory	India
City	Noida
Period	13/10/22 → 14/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 13 Climate Action

Keywords

climate change
innovation
research and development
resource efficiency
science corporation agreements
technological progress

ASJC Scopus subject areas

Control and Optimization
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Information Systems and Management

Access to Document

10.1109/ICRITO56286.2022.9964662

Cite this

Lodhi, S., Sakshi, S., Kukreja, V., & Bansal, A. (2022). Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022 (2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICRITO56286.2022.9964662

Lodhi, Sachin ; Sakshi, Sakshi ; Kukreja, Vinay et al. / Applying Deep Learning in Mars Exploration : A Neural Network-based Study to Classify Martian Terrain Features. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022. Institute of Electrical and Electronics Engineers, 2022. (2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022).

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title = "Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features",

abstract = "The red planet has persisted in being the top destination for space exploration and has also fascinated researchers to experiment with frequently launched its dataset of images. NASA has launched various missions on the track of space expeditions to explore the surface and the environment of the potential life-sustaining planet. Among all the missions for capturing images of the Martian terrain, the HiRISE experiment has been successful in coming up with the best datasets featuring the surface of Mars. In this research study, the authors have targeted the classification of this high-quality image dataset produced as the outcome of the HiRISE using a deep learning model. The proposed model is convoluted and built with the help of neural network layers (neuron layers). Based on the classification, the authors were able to classify the images of 7 terrain features of Mars, namely Crater, Impact Ejecta, Bright Dune, Dark Dune, Slope Streak, Spider, and Swiss Cheese. And the classification accuracy achieved by the model is 94.8\%.",

keywords = "climate change, innovation, research and development, resource efficiency, science corporation agreements, technological progress",

author = "Sachin Lodhi and Sakshi Sakshi and Vinay Kukreja and Ankit Bansal",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022 ; Conference date: 13-10-2022 Through 14-10-2022",

year = "2022",

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doi = "10.1109/ICRITO56286.2022.9964662",

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Lodhi, S, Sakshi, S, Kukreja, V & Bansal, A 2022, Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features. in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022, Institute of Electrical and Electronics Engineers, 10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022, Noida, India, 13/10/22. https://doi.org/10.1109/ICRITO56286.2022.9964662

Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features. / Lodhi, Sachin; Sakshi, Sakshi; Kukreja, Vinay et al.
2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022. Institute of Electrical and Electronics Engineers, 2022. (2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Applying Deep Learning in Mars Exploration

T2 - 10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

AU - Lodhi, Sachin

AU - Sakshi, Sakshi

AU - Kukreja, Vinay

AU - Bansal, Ankit

PY - 2022/1/1

Y1 - 2022/1/1

N2 - The red planet has persisted in being the top destination for space exploration and has also fascinated researchers to experiment with frequently launched its dataset of images. NASA has launched various missions on the track of space expeditions to explore the surface and the environment of the potential life-sustaining planet. Among all the missions for capturing images of the Martian terrain, the HiRISE experiment has been successful in coming up with the best datasets featuring the surface of Mars. In this research study, the authors have targeted the classification of this high-quality image dataset produced as the outcome of the HiRISE using a deep learning model. The proposed model is convoluted and built with the help of neural network layers (neuron layers). Based on the classification, the authors were able to classify the images of 7 terrain features of Mars, namely Crater, Impact Ejecta, Bright Dune, Dark Dune, Slope Streak, Spider, and Swiss Cheese. And the classification accuracy achieved by the model is 94.8%.

AB - The red planet has persisted in being the top destination for space exploration and has also fascinated researchers to experiment with frequently launched its dataset of images. NASA has launched various missions on the track of space expeditions to explore the surface and the environment of the potential life-sustaining planet. Among all the missions for capturing images of the Martian terrain, the HiRISE experiment has been successful in coming up with the best datasets featuring the surface of Mars. In this research study, the authors have targeted the classification of this high-quality image dataset produced as the outcome of the HiRISE using a deep learning model. The proposed model is convoluted and built with the help of neural network layers (neuron layers). Based on the classification, the authors were able to classify the images of 7 terrain features of Mars, namely Crater, Impact Ejecta, Bright Dune, Dark Dune, Slope Streak, Spider, and Swiss Cheese. And the classification accuracy achieved by the model is 94.8%.

KW - climate change

KW - innovation

KW - research and development

KW - resource efficiency

KW - science corporation agreements

KW - technological progress

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U2 - 10.1109/ICRITO56286.2022.9964662

DO - 10.1109/ICRITO56286.2022.9964662

M3 - Conference contribution

AN - SCOPUS:85144593315

T3 - 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022

BT - 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022

PB - Institute of Electrical and Electronics Engineers

Y2 - 13 October 2022 through 14 October 2022

ER -

Lodhi S, Sakshi S, Kukreja V, Bansal A. Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022. Institute of Electrical and Electronics Engineers. 2022. (2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022). doi: 10.1109/ICRITO56286.2022.9964662

Applying Deep Learning in Mars Exploration: A Neural Network-based Study to Classify Martian Terrain Features

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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