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Attention-Aware Pseudo-3D Convolutional Neural Network for Hyperspectral Image Classification

Lin, Jianzhe and Mou, LiChao and Zhu, Xiao Xiang and Ji, Xiangyang and Wang, Z. Jane (2021) Attention-Aware Pseudo-3D Convolutional Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3038212. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9347797


Convolutional neural networks (CNNs) have been applied for hyperspectral image classification recently. Among this class of deep models, 3-D CNN has been shown to be more effective by learning discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). However, by simply imposing 3-D CNN to HSI, a large amount of initial information might be lost in this CNN pipeline. The proposed attention-aware pseudo-3-D (AP3D) convolutional network for HSI classification is motivated by two observations. First, each dimension of the 3-D HSI is not equally important, different attention should be paid to different dimensions of the initial HSI image, especially in the first convolution operation. Second, intermediate representations of the 3-D input image at different stages in the 3-D CNN pipeline represent different levels of features and should not be neglected and abandoned. Instead, a 2-D matrix of scores for each feature map should be fed to the final softmax layer. Quantitative and qualitative results demonstrate that the proposed AP3D model outperforms the state-of-the-art HSI classification methods in agricultural and rural/urban data sets: Indian Pines, Pavia University, and Salinas Scene.

Item URL in elib:https://elib.dlr.de/140912/
Document Type:Article
Title:Attention-Aware Pseudo-3D Convolutional Neural Network for Hyperspectral Image Classification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Wang, Z. JaneTsinghua UniversityUNSPECIFIED
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2020.3038212
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Status:In Press
Keywords:Hyperspectral image, salient samples, super-vised classification, transfer learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 17:46
Last Modified:12 Feb 2021 17:46

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