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

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.