TY - JOUR

T1 - Adversarial discriminative domain adaptation for deforestation detection

AU - Noa, J.

AU - Soto, P. J.

AU - Costa, G. A.O.P.

AU - Wittich, Dennis

AU - Feitosa, R. Q.

AU - Rottensteiner, Franz

N1 - Funding Information: The authors would like to thank the founding provided by Coorde-nac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Fundac¸ão de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).

PY - 2021/6/17

Y1 - 2021/6/17

N2 - Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.

AB - Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.

KW - Change detection

KW - Deep learning

KW - Deforestation

KW - Domain adaptation

KW - Margin-based regularization

UR - http://www.scopus.com/inward/record.url?scp=85113160239&partnerID=8YFLogxK

U2 - 10.5194/isprs-annals-V-3-2021-151-2021

DO - 10.5194/isprs-annals-V-3-2021-151-2021

M3 - Conference article

AN - SCOPUS:85113160239

VL - 5

SP - 151

EP - 158

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9042

IS - 3

T2 - 24th ISPRS Congress on Imaging today, foreseeing tomorrow, Commission III

Y2 - 5 July 2021 through 9 July 2021

ER -