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- Title
DEFORESTATION DETECTION WITH WEAK SUPERVISED CONVOLUTIONAL NEURAL NETWORKS IN TROPICAL BIOMES.
- Authors
Soto, P. J.; Costa, G. A. O. P.; Ortega, M. X.; Bermudez, J. D.; Feitosa, R. Q.
- 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.
- Subjects
CONVOLUTIONAL neural networks; DEFORESTATION; BIOMES; DEEP learning; VECTOR analysis
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2022, Issue B3, p713
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLIII-B3-2022-713-2022