Deforestation Detection With Weak Supervised Convolutional Neural Networks In Tropical Biomes
Type | Article | ||||||||
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Date | 2022 | ||||||||
Language | English | ||||||||
Author(s) | Soto Vega Pedro Juan1, Costa G. A. O. P.2, Ortega M. X.3, Bermudez J. D.3, Feitosa R. Q.3 | ||||||||
Affiliation(s) | 1 : Ifremer, PDG-REM-EEP-LEP, F-29280 Plouzan´e, France 2 : Dept. of Informatics and Computer Science, Rio de Janeiro State University (UERJ), Brazil 3 : Dept. of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil |
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Meeting | XXIV ISPRS Congress (2022 edition) : Imaging Today, Foreseeing Tomorrow, Commission III, 6–11 June 2022, Nice, France | ||||||||
Source | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (1682-1750) (Copernicus GmbH), 2022 , Vol. XLIII-B3-2022 , P. 713-719 | ||||||||
DOI | 10.5194/isprs-archives-XLIII-B3-2022-713-2022 | ||||||||
Keyword(s) | Change detection, deep learning, domain adaptation, deforestation, weak supervision | ||||||||
Abstract | Deep learning methods are known to demand large amounts of labeled samples for training. For remote sensing applications such as change detection, coping with that demand is expensive and time-consuming. This work aims at investigating a noisy-label-based weak supervised method in the context of a deforestation mapping application, characterized by a high class imbalance between the classes of interest, i.e., deforestation and no-deforestation. The study sites correspond to different regions in the Amazon and Brazilian Cerrado biomes. To mitigate the lack of ground-truth labeled training samples, we devised an unsupervised pseudo-labeling scheme based on the Change Vector Analysis technique. The experimental results indicate that the proposed approach can improve the accuracy of deforestation detection applications. |
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