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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

Abstract

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
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Lin, JianzheUNSPECIFIEDUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Ji, XiangyangUNSPECIFIEDUNSPECIFIED
Wang, Z. JaneTsinghua UniversityUNSPECIFIED
Date:2021
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2020.3038212
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
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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