使用残差增强特征融合超图神经网络进行高光谱图像分类

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-03-05 DOI:10.1080/2150704x.2024.2320177

Yanhong Yang, Danyang Li, Hongtao Wang, Yuan Feng, Lei Yan, Guodao Zhang

引用次数: 0

摘要

超图神经网络（HyperGraph Neural Network，HGNN）是最近出现的一种用于高光谱图像分类（HSIC）的有前途的方法，它兼具最先进的性能和强大的表示能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Hyperspectral image classification using a residual enhanced feature fusion hypergraph neural network

HyperGraph Neural Network (HGNN) has recently emerged as a promising approach for hyperspectral image classification (HSIC), reconciling state-of-the-art performance with powerful representation ca...

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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.