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MultiScene: A Large-scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images

Hua, Yuansheng and Mou, LiChao and Jin, Pu and Zhu, Xiao Xiang (2022) MultiScene: A Large-scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2021.3110314. ISSN 0196-2892. (In Press)

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/9537917

Abstract

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task -- multi-scene recognition in single images. To this end, we create a large-scale dataset, called MultiScene, composed of 100,000 unconstrained high-resolution aerial images. Considering that manually labeling such images is extremely arduous, we resort to low-cost annotations from crowdsourcing platforms, e.g., OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and incorrectness, which introduce noise into image labels. To address this issue, we visually inspect 14,000 images and correct their scene labels, yielding a subset of cleanly-annotated images, named MultiScene-Clean. With it, we can develop and evaluate deep networks for multi-scene recognition using clean data. Moreover, we provide crowdsourced annotations of all images for the purpose of studying network learning with noisy labels. We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively.

Item URL in elib:https://elib.dlr.de/145753/
Document Type:Article
Title:MultiScene: A Large-scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hua, YuanshengYuansheng.Hua (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.dehttps://orcid.org/0000-0001-8407-6413
Jin, Pupu.jin (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2022
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
DOI :10.1109/TGRS.2021.3110314
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:Computer Vision, Pattern Recognition
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:19 Nov 2021 09:23
Last Modified:14 Jan 2022 14:55

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