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Weakly supervised semantic segmentation for remote sensing hyperspectral imaging

Author
Moliner, E.; Salgueiro, L.; Vilaplana, V.
Type of activity
Presentation of work at congresses
Name of edition
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing
Date of publication
2020
Presentation's date
2020-05-06
Book of congress proceedings
2020 IEEE International Conference on Acoustics, Speech,and Signal Processing: proceedings: May 4-8, 2020: Centre de Convencions Internacional de Barcelona (CCIB) Barcelona, Spain
First page
2273
Last page
2277
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.1109/ICASSP40776.2020.9053384
Project funding
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/192482 Open in new window
URL
https://ieeexplore.ieee.org/document/9053384 Open in new window
Abstract
This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better tha...
Citation
Moliner, E.; Salgueiro, L.; Vilaplana, V. Weakly supervised semantic segmentation for remote sensing hyperspectral imaging. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2020 IEEE International Conference on Acoustics, Speech,and Signal Processing: proceedings: May 4-8, 2020: Centre de Convencions Internacional de Barcelona (CCIB) Barcelona, Spain". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 2273-2277.
Keywords
Hyperspectral image, Remote sensing, Weakly-supervised segmentation
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants