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: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 752-761. International Conference on Computer Vision (ICCV), Virtuell. doi: 10.1109/ICCVW54120.2021.00089.
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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/ | ||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||
Title: | Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image | ||||||||||||||||||
Authors: |
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Date: | 2021 | ||||||||||||||||||
Journal or Publication Title: | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | ||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||
In SCOPUS: | No | ||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||
DOI: | 10.1109/ICCVW54120.2021.00089 | ||||||||||||||||||
Page Range: | pp. 752-761 | ||||||||||||||||||
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 | ||||||||||||||||||
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 09:43 | ||||||||||||||||||
Last Modified: | 20 Jul 2022 12:35 |
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