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LahNet: A Convolutional Neural Network Fusing Low- and High-Level Features for Aerial Scene Classification

Hua, Yuansheng and Mou, LiChao and Zhu, Xiao Xiang (2018) LahNet: A Convolutional Neural Network Fusing Low- and High-Level Features for Aerial Scene Classification. In: 2018 International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4728-4731. IGARSS 2018, 23.- 27. Juli 2018, Valencia, Spain. doi: 10.1109/IGARSS.2018.8519576.

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Official URL: https://ieeexplore.ieee.org/document/8519576


In this paper, we proposed an innovative end-to-end convolutional neural network (CNN), which is trained to learn how to fuse multi-level features for aerial scene classification. Instead of using only coarse semantic features as conventional CNNs, we resort to first hierarchically extracting dense highlevel features and then element-wise fusing them with lowlevel features to build a comprehensive feature representation, which contains not only high-level semantic information but also fine-grained low-level details, for scene classification. The network is evaluated on two broadly used aerial scene datasets, UCM and AID. The experimental results indicate that the proposed LAHNet performs superiorly compared to the existing benchmark methods. Furthermore, visualization of the fused features presents an intuitive illustration of the remarkable improvement.

Item URL in elib:https://elib.dlr.de/134066/
Document Type:Conference or Workshop Item (Poster)
Title:LahNet: A Convolutional Neural Network Fusing Low- and High-Level Features for Aerial Scene Classification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hua, YuanshengYuansheng.Hua (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Journal or Publication Title:2018 International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IGARSS.2018.8519576
Page Range:pp. 4728-4731
EditorsEmailEditor's ORCID iD
Keywords:convolutional neural network (CNN), feature fusion, aerial scene classification
Event Title:IGARSS 2018
Event Location:Valencia, Spain
Event Type:international Conference
Event Dates:23.- 27. Juli 2018
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 - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:11 Feb 2020 09:39
Last Modified:11 Feb 2020 09:39

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