Hierarchical Prototype-Aligned Graph Neural Network for Cross-Scene Hyperspectral Image Classification

Author:

Shen Danyao1,Hu Haojie1ORCID,He Fang1ORCID,Zhang Fenggan1,Zhao Jianwei1,Shen Xiaowei1

Affiliation:

1. Xi’an Research Institute of High Technology, Xi’an 710025, China

Abstract

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks.

Publisher

MDPI AG

Reference35 articles.

1. Face recognition based on improved Retinex and sparse representation;Li;Procedia Eng.,2011

2. Zhang, L., Yang, M., and Feng, X. (2011, January 6–13). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.

3. Structure-aware collaborative representation for hyperspectral image classification;Li;IEEE Trans. Geosci. Remote Sens.,2019

4. Discrete and Balanced Spectral Clustering with Scalability;Wang;IEEE Trans. Pattern Anal. Mach. Intell.,2023

5. Diverse region-based CNN for hyperspectral image classification;Zhang;IEEE Trans. Image Process.,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3