DA-IMRN: Dual-Attention-Guided Interactive Multi-Scale Residual Network for Hyperspectral Image Classification
department or school: Applied Science, Faculty of
department or school: Non UBC
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
identifier citation: Remote Sensing 14 (3): 530 (2022)
creator: Zou, Liang
creator: Zhang, Zhifan
creator: Du, Haijia
creator: Lei, Meng
creator: Xue, Yong
creator: Wang, Z. Jane
issued: 2022-03-29T15:06:15Z
issued: 2022-01-23
description: Deep learning-based fusion of spectral-spatial information is increasingly dominant for hyperspectral image (HSI) classification. However, due to insufficient samples, current feature fusion methods often neglect joint interactions. In this paper, to further improve the classification accuracy, we propose a dual-attention-guided interactive multi-scale residual network (DA-IMRN) to explore the joint spectral-spatial information and assign pixel-wise labels for HSIs without information leakage. In DA-IMRN, two branches focusing on spatial and spectral information separately are employed for feature extraction. A bidirectional-attention mechanism is employed to guide the interactive feature learning between two branches and promote refined feature maps. In addition, we extract deep multi-scale features corresponding to multiple receptive fields from limited samples via a multi-scale spectral/spatial residual block, to improve classification performance. Experimental results on three benchmark datasets (i.e., Salinas Valley, Pavia University, and Indian Pines) support that attention-guided multi-scale feature learning can effectively explore the joint spectral-spatial information. The proposed method outperforms state-of-the-art methods with the overall accuracy of 91.26%, 93.33%, and 82.38%, and the average accuracy of 94.22%, 89.61%, and 80.35%, respectively.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/81048
note: Citation: Zou, L.; Zhang Z.; Du, H.;Lei, M.; Xue, Y.; Wang, Z.J. DA-IMRN:Dual-Attention-Guided InteractiveMulti-Scale Residual Network forHyperspectral Image Classification.Remote Sens. 2022, 14, 530. https://doi.org/10.3390/rs14030530Academic Editors: Xiaoli Li,Zhenghua Chen, Min Wu andJianfei YangReceived: 13 December 2021Accepted: 19 January 2022Published: 23 January 2022Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2022 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).remote sensing ArticleDA-IMRN: Dual-Attention-Guided Interactive Multi-ScaleResidual Network for Hyperspectral Image ClassificationLiang Zou 1 , Zhifan Zhang 1, Haijia Du 1, Meng Lei 1,∗ , Yong Xue 2,3 and Z. Jane Wang 41 Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education,China University of Mining and Technology, Xuzhou 221116, China; liangzou@cumt.edu.cn (L.Z.);zfzhang@cumt.edu.cn (Z.Z.); hjdu@cumt.edu.cn (H.D.)2 School of Environment Science and Spatial Informatics, China University of Mining and Technology,Xuzhou 221116, China; yxue@cumt.edu.cn3 School of Electronics, Computing and Mathematics, University of Derby, Derby DE22 1GB, UK4 Department of Electrical and Computer Engineering, University of British Columbia,Vancouver, BC V6T 1Z4, Canada; zjanew@ece.ubc.ca* Correspondence: lmsiee@cumt.edu.cnAbstract: Deep learning-based fusion of spectral-spatial information is increasingly dominant forhyperspectral image (HSI) classification. However, due to insufficient samples, current feature fusionmethods often neglect joint interactions. In this paper, to further improve the classification accuracy,we propose a dual-attention-guided interactive multi-scale residual network (DA-IMRN) to explor…
has type: Article
is shown at: 10.14288/1.0407443
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/rs14030530
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: hyperspectral image classification
subject: interaction
subject: dual-attention mechanism
subject: multi-scale spectral/spatial residual block
type: Text
identifier URI: http://hdl.handle.net/2429/81048