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Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image

Nouman, Ahmed and Saha, Sudipan and Shahzad, Muhammad and Moazam Fraz, Muhammad and Zhu, Xiao Xiang (2021) Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image. In: 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, pp. 752-761. International Conference on Computer Vision (ICCV), 2021-10-11 - 2021-10-17, Virtuell. doi: 10.1109/ICCVW54120.2021.00089. ISBN 978-166540191-3. ISSN 1550-5499.

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Official URL: https://ieeexplore.ieee.org/document/9607401

Abstract

Automated forest mapping is important to understand our forests that play a key role in ecological system. However, efforts towards forest mapping is impeded by difficulty to collect labeled forest images that show large intraclass variation. Recently unsupervised learning has shown promising capability when exploiting limited labeled data. Motivated by this, we propose a progressive unsupervised deep transfer learning method for forest mapping. The proposed method exploits a pre-trained model that is subsequently fine-tuned over the target forest domain. We propose two different fine-tuning echanism, one works in a totally unsupervised setting by jointly learning the parameters of CNN and the k-means based cluster assignments of the resulting features and the other one works in a semi-supervised setting by exploiting the extracted k-nearest neighbor based pseudo labels. The proposed progressive scheme is evaluated on publicly available EuroSAT dataset using the relevant base model trained on BigEarth-Net labels. The results show that the proposed method greatly improves the forest regions classification accuracy as compared to the unsupervised baseline, nearly approaching the supervised classification approach.

Item URL in elib:https://elib.dlr.de/145759/
Document Type:Conference or Workshop Item (Speech)
Title:Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Nouman, AhmedUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Saha, SudipanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Shahzad, MuhammadUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Moazam Fraz, MuhammadUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDhttps://orcid.org/0000-0001-5530-3613UNSPECIFIED
Date:2021
Journal or Publication Title:18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/ICCVW54120.2021.00089
Page Range:pp. 752-761
ISSN:1550-5499
ISBN:978-166540191-3
Status:Published
Keywords:Unsupervised Learning, Deep Learning, Forest Monitoring, AI4EO, Earth Observation, Transfer Learning
Event Title:International Conference on Computer Vision (ICCV)
Event Location:Virtuell
Event Type:international Conference
Event Start Date:11 October 2021
Event End Date:17 October 2021
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, Dr. Anja
Deposited On:19 Nov 2021 09:43
Last Modified:05 Jun 2024 12:10

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