@misc{88682, type = "Article", year = "2022", title = "Deforestation Detection With Weak Supervised Convolutional Neural Networks In Tropical Biomes", journal = "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", editor = "Copernicus GmbH", volume = "XLIII-B3-2022", number = "", pages = "713-719", author = "Soto Vega Pedro Juan, Costa G. A. O. P., Ortega M. X., Bermudez J. D., Feitosa R. Q.", url = "https://archimer.ifremer.fr/doc/00775/88682/", organization = "", address = "FRANCE, BRAZIL", doi = "https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-713-2022", 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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