Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Zhang, Yuxiang; Li, Wei; Zhang, Mengmeng; Wang, Shuai; Tao, Ran; Du, Qian

doi:10.1109/tnnls.2022.3185795

articleIEEE Transactions on Neural Networks and Learning SystemsJun 30, 2022Closed access

Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

YZYuxiang Zhang WLWei Li MZMengmeng Zhang SWShuai Wang RTRan Tao

Beijing Institute of Technology · University of Hong Kong · +1 more institution

PubMed

Indexed incrossrefpubmed

Abstract

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification focus on cases where source data (SD) and target data (TD) with the same classes are obtained by the same sensor. However, the classification performance is significantly reduced when there are new classes in TD. In addition, domain alignment, as one of the main approaches in DA, is carried out based on local spatial information, rarely taking into account nonlocal spatial information (nonlocal relationships) with strong correspondence. A graph information aggregation cross-domain few-shot learning (Gia-CFSL) framework is proposed, intending to make up for the above-mentioned shortcomings by combining FSL with domain…

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Topics & keywords

Topics

Keywords

Hyperspectral imaging
Computer science
Graph
Artificial intelligence
Pattern recognition (psychology)
Domain (mathematical analysis)
Image (mathematics)
Mathematics

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