elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

A Multi-Spectral and Multi-Angle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images over Urban Areas

Huang, Xin and Li, Shuang and Li, Jiayi and Zhu, Xiao Xiang and Benediktsson, Jon Atli (2021) A Multi-Spectral and Multi-Angle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing, pp. 1-20. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3037211. ISSN 0196-2892. (In Press)

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/9266127

Abstract

The recent availability of high-resolution multiview ZY-3 satellite images, with angular information, can provide an opportunity to capture 3-D structural features for classification. In high-resolution image classification over urban areas, objects with diverse vertical structures make urban landscape more heterogeneous in 3-D space and consequently can make the classification challenging. In this article, a novel multiangle gray-level cooccurrence tensor feature is proposed based on the multiview bands of the ZY-3 imagery, namely, GLCMMA-T. The GLCMMA-T feature captures the distributions of the gray-level spatial variation under different viewing angles, which can depict the 3-D textures and structures of urban objects. The spectral and GLCMMA-T tensor features are interpreted by two 3-D convolutional neural network (CNN) streams and then concatenated as the input to the fully connected layer. This novel multispectral and multiangle 3-D convolutional neural network (M²-3-DCNN) combines the spectral and angular information, and the fused feature has the potential to provide a comprehensive description of urban objects with complex vertical structures. The experimental results on ZY-3 multiview images from four test areas indicate that the proposed method can significantly improve the classification accuracy when compared with several state-of-the-art multiangle features and deep-learning-based image classification methods.

Item URL in elib:https://elib.dlr.de/138665/
Document Type:Article
Title:A Multi-Spectral and Multi-Angle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images over Urban Areas
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Huang, XinUNSPECIFIEDhttps://orcid.org/0000-0002-5625-0338
Li, ShuangUNSPECIFIEDUNSPECIFIED
Li, JiayiUNSPECIFIEDUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Benediktsson, Jon AtliFaculty of Electrical and Computer Engineering, Ubiversity of IcelandUNSPECIFIED
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.2020.3037211
Page Range:pp. 1-20
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:3D convolutional network, ZY-3, satellite images, classification, urban areas
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:30 Nov 2020 18:15
Last Modified:14 Jan 2021 13:33

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.