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Erschienen in:

2024 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang

Erschienen in: Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

Verlag: Springer Nature Singapore

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Abstract

Remote Sensing (RS) is a non-contact earth observation technology that obtains ground target information through the reflection or radiation of electromagnetic waves on ground objects (SambhuNath in An introduction to remote sensing. Koros, 2014; Brink et al. in Introduction to remote sensing for conservation practitioners, 2018). Military remote sensing technology can use remote sensing carriers, e.g., satellites and unmanned aerial vehicles (UAVs), to carry out high-resolution, real-time, multi-angle and multi-frequency remote sensing observation and acquisition of enemy military targets. Then, the military intelligence of enemy and battlefield conditions can be monitored, which provides intelligence support and tactical guidance for military command departments. Therefore, this technology holds a pivotal position in modern warfare, and is of great significance in improving combat efficiency and reducing war costs. In the Russia–Ukraine conflict, both sides have realized the importance of high-precision remote sensing images in obtaining the military deployment trends of the other side, and they compete to use remote sensing technology to timely understand each other’s dynamics and develop corresponding strategies accordingly. At the same time, satellite remote sensing images have become the best tool for netizens and journalists to understand and analyze the conflict situation in Ukraine, enabling remote sensing technology to gain widespread attention in public opinion.

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Nächstes Kapitel Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification
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Metadaten
Titel
Introduction
verfasst von
Yao Ding
Zhili Zhang
Haojie Hu
Fang He
Shuli Cheng
Yijun Zhang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

Print ISBN: 978-981-9780-08-2

Electronic ISBN: 978-981-9780-09-9

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-8009-9_1

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