Quantum Neural Networks for Enhanced Crater and Boulder Detection Using Hyper Spectral Imaging
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Student Project | Earth Science and Engineering | India | Volume 14 Issue 2, February 2025 | Popularity: 3.8 / 10


     

Quantum Neural Networks for Enhanced Crater and Boulder Detection Using Hyper Spectral Imaging

Priyanshu Halder


Abstract: This study presents an advanced approach to detecting and analyzing craters and boulders using quantum neural networks and hyper spectral imaging (HSI). By leveraging pixel-by-pixel classification through semantic segmentation, our method accurately determines the edges and depths of geological features. The use of a custom quantum-based neural network with an n?n architecture enhances edge detection and reduces processing time, achieving an accuracy rate of 80%. The proposed algorithm efficiently converts RGB images into HSI data for in-depth spectral analysis, surpassing traditional Geographic Information Systems (GIS) techniques. Additionally, our approach integrates cognitive neural networks and advanced data servers to optimize location detection within a defined azimuth range. This research highlights the effectiveness of quantum-driven methodologies in improving spatial resolution and analytical precision, paving the way for enhanced geological feature classification in remote sensing applications.


Keywords: QGIS Software, IBM Qiskit, Quantum Circuit, Nanosatellite


Edition: Volume 14 Issue 2, February 2025


Pages: 176 - 180


DOI: https://www.doi.org/10.21275/SR25203105727



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Priyanshu Halder, "Quantum Neural Networks for Enhanced Crater and Boulder Detection Using Hyper Spectral Imaging", International Journal of Science and Research (IJSR), Volume 14 Issue 2, February 2025, pp. 176-180, https://www.ijsr.net/getabstract.php?paperid=SR25203105727, DOI: https://www.doi.org/10.21275/SR25203105727