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Semantic segmentation of remote sensing images with sparse annotations

Hua, Yuansheng and Marcos, Diego and Mou, LiChao and Zhu, Xiao Xiang and Tuia, Devis (2022) Semantic segmentation of remote sensing images with sparse annotations. IEEE Geoscience and Remote Sensing Letters, 19, p. 8006305. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2021.3051053. ISSN 1545-598X.

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Official URL: https://ieeexplore.ieee.org/document/9335495


Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremelylabor-intensive and time-consuming to produce. Moreover, professional photograph interpreters might have to be involved in guaranteeing the correctness of annotations. To alleviate such a burden, we propose a framework for semantic segmentation of aerial images based on incomplete annotations, where annotatorsare asked to label a few pixels with easy-to-draw scribbles. To exploit these sparse scribbled annotations, we propose theFEature and Spatial relaTional regulArization (FESTA) method to complement the supervised task with an unsupervised learn-ing signal that accounts for neighborhood structures both inspatial and feature terms. For the evaluation of our frame-work, we perform experiments on two remote sensing image segmentation data sets involving aerial and satellite imagery, respectively. Experimental results demonstrate that the exploitation of sparse annotations can significantly reduce labeling costs, while the proposed method can help improve the performance of semantic segmentation when training on such annotations. The sparse labels and codes are publicly available for reproducibility purposes.

Item URL in elib:https://elib.dlr.de/140911/
Document Type:Article
Title:Semantic segmentation of remote sensing images with sparse annotations
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
Date:January 2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2021.3051053
Page Range:p. 8006305
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Aerial image, convolutional neural networks (CNNs), semantic segmentation, semisupervised learning, sparse scribbled annotation
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: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 17:41
Last Modified:14 Jan 2022 14:54

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