MULTI-SPECTRAL IMAGE CLASSIFICATION WITH QUANTUM NEURAL NETWORK

被引：14

作者：

Gawron, Piotr ^{[1
]}

Lewinski, Stanislaw ^{[2
]}

机构：

[1] Polish Acad Sci, Nicolaus Copernicus Astron Ctr, Ul Rektorska 4, PL-00614 Warsaw, Poland

[2] Polish Acad Sci, Space Res Ctr, Ul Bartycka 18A, PL-00716 Warsaw, Poland

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

land cover classification; multi-spectral imagery; quantum machine learning;

D O I：

10.1109/IGARSS39084.2020.9323065

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：3513 / 3516

页数：4

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