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Relation Network for Multi-label Aerial Image Classification

Hua, Yuansheng and Mou, LiChao and Zhu, Xiao Xiang (2020) Relation Network for Multi-label Aerial Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 58 (7), pp. 4558-4572. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2019.2963364. ISSN 0196-2892.

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8986556

Abstract

Multilabel classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an long short term memory (LSTM) layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multilabel classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module; 2) an attentional region extraction module; and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features without region proposal generation, yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on two multilabel aerial image data sets, of which one is newly produced. Quantitative and qualitative results on these two data sets demonstrate the effectiveness of our model. To facilitate progress in the multilabel aerial image classification, our produced data set will be made publicly available.

Item URL in elib:https://elib.dlr.de/140905/
Document Type:Article
Title:Relation Network for Multi-label Aerial Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hua, YuanshengUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mou, LiChaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:58
DOI:10.1109/TGRS.2019.2963364
Page Range:pp. 4558-4572
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Attentional region extraction, convolutional neural network (CNN), high-resolution aerial image, label relational reasoning, multilabel classification
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
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 16:47
Last Modified:24 Oct 2023 12:56

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