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A Co-learning Method to Utilize Optical Images and Photogrammetric Point Clouds for Building Extraction

Xie, Yuxing and Tian, Jiaojiao and Zhu, Xiao Xiang (2023) A Co-learning Method to Utilize Optical Images and Photogrammetric Point Clouds for Building Extraction. International Journal of Applied Earth Observation and Geoinformation, 116, p. 103165. Elsevier. doi: 10.1016/j.jag.2022.103165. ISSN 1569-8432.

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Official URL: https://www.sciencedirect.com/science/article/pii/S1569843222003533

Abstract

Although deep learning techniques have brought unprecedented accuracy to automatic building extraction, several main issues still constitute an obstacle to effective and practical applications. The industry is eager for higher accuracy and more flexible data usage. In this paper, we present a co-learning framework applicable to building extraction from optical images and photogrammetric point clouds, which can take the advantage of 2D/3D multimodality data. Instead of direct information fusion, our co-learning framework adaptively exploits knowledge from another modality during the training phase with a soft connection, via a predefined loss function. Compared to conventional data fusion, this method is more flexible, as it is not mandatory to provide multimodality data in the test phase. We propose two types of co-learning: a standard version and an enhanced version, depending on whether unlabeled training data are employed. Experimental results from two data sets show that the methods we present can enhance the performance of both image and point cloud networks in few-shot tasks, as well as image networks when applying fully labeled training data sets.

Item URL in elib:https://elib.dlr.de/192609/
Document Type:Article
Title:A Co-learning Method to Utilize Optical Images and Photogrammetric Point Clouds for Building Extraction
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Xie, YuxingUNSPECIFIEDhttps://orcid.org/0000-0002-6408-5109UNSPECIFIED
Tian, JiaojiaoUNSPECIFIEDhttps://orcid.org/0000-0002-8407-5098UNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDhttps://orcid.org/0000-0001-5530-3613UNSPECIFIED
Date:February 2023
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:116
DOI:10.1016/j.jag.2022.103165
Page Range:p. 103165
Publisher:Elsevier
ISSN:1569-8432
Status:Published
Keywords:building extraction; co-learning; multimodality learning; multispectral images; point clouds; remote sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - V&V4NGC - Methoden, Prozesse und Werkzeugketten für die Validierung & Verifikation von NGC, R - Optical remote sensing, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Xie, Yuxing
Deposited On:05 Jan 2023 08:43
Last Modified:12 Jan 2024 09:26

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