Multiscale Dense Cross-Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification

Author:

Liu Runmin12ORCID,Ning Xin3ORCID,Cai Weiwei4ORCID,Li Guangjun1ORCID

Affiliation:

1. College of Sports Engineering & Information Technology, Wuhan Sports University, Wuhan 430079, China

2. School of Graduate, Wuhan Sports University, Wuhan 430079, China

3. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

4. Central South University of Forestry and Technology, Changsha 410004, China

Abstract

In recent years, learning algorithms based on deep convolution frameworks have gradually become the research hotspots in hyperspectral image classification tasks. However, in the classification process, high-dimensionality problems with large amounts of data and feature redundancy with interspectral correlation of hyperspectral images have not been solved efficiently. Therefore, this paper investigates data dimensionality reduction and feature extraction and proposes a novel multiscale dense cross-attention mechanism algorithm with covariance pooling (MDCA-CP) for hyperspectral image scene classification. The multisize convolution module can detect subtle changes in the hyperspectral images’ spatial and spectral dimensions between the pixels in the local areas and are suitable for extracting hyperspectral data with complex and diverse types of structures. For traditional algorithms that assign attention weights in a one-way manner, thus leading to the loss of feature information, the dense cross-attention mechanism proposed in this study can jointly distribute the attention weights horizontally and vertically to efficiently capture the most representative features. In addition, this study also uses covariance pooling to further extract the features of hyperspectral images from the second order. Experiments have been conducted on three well-known hyperspectral datasets, and the results thus obtained show that the MDCA-CP algorithm is superior compared to the other well-known methods.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Cited by 41 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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