Multi-Spectral Image Classification with Quantum Neural Network
Piotr Gawron, Stanisław Lewiński
Abstract
Processing Earth observation images to obtain land cover classification is an important task allowing to track changes on the Earth's surface resulting from natural processes, human activity, and climate change. The amount of data acquired from Earth observation satellites is very large and their processing takes large amount of computational resources. We investigate application of quantum circuit based neural network classifiers for multi-spectral data classification aimed at obtaining the land cover information. We show a proof-of-concept of our approach.
Topics & Concepts
Computer scienceLand coverArtificial neural networkEarth observationContextual image classificationTask (project management)Artificial intelligenceCover (algebra)Remote sensingPattern recognition (psychology)Image (mathematics)Land useSatelliteGeologyEconomicsAerospace engineeringEngineeringManagementCivil engineeringMechanical engineeringQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyNeural Networks and Applications