FN Archimer Export Format
PT J
TI Deforestation Detection With Weak Supervised Convolutional Neural Networks In Tropical Biomes
BT 
AF SOTO VEGA, Pedro Juan
   Costa, G. A. O. P.
   Ortega, M. X.
   Bermudez, J. D.
   Feitosa, R. Q.
AS 1:1;2:2;3:3;4:3;5:3;
FF 1:PDG-REM-BEEP-LEP;2:;3:;4:;5:;
C1 Ifremer, PDG-REM-EEP-LEP, F-29280 Plouzan´e, France
   Dept. of Informatics and Computer Science, Rio de Janeiro State University (UERJ), Brazil
   Dept. of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
C2 IFREMER, FRANCE
   UNIV RIO DE JANEIRO, BRAZIL
   UNIV RIO DE JANEIRO, BRAZIL
SI BREST
SE PDG-REM-BEEP-LEP
UM BEEP-LM2E
IN WOS Ifremer UMR
   DOAJ
   copubli-int-hors-europe
   copubli-sud
TC 1
UR https://archimer.ifremer.fr/doc/00775/88682/94396.pdf
LA English
DT Article
DE ;Change detection;deep learning;domain adaptation;deforestation;weak supervision
AB 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. 
PY 2022
SO The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN 1682-1750
PU Copernicus GmbH
VL XLIII-B3-2022
UT 000855647800099
BP 713
EP 719
DI 10.5194/isprs-archives-XLIII-B3-2022-713-2022
ID 88682
ER

EF