Domain adversarial neural network-based oil palm detection using high-resolution satellite images

Wenzhao Wu; Juepeng Zheng; Weijia Li; Haohuan Fu; Shuai Yuan; Le Yu

doi:10.1117/12.2557829

24 April 2020 Domain adversarial neural network-based oil palm detection using high-resolution satellite images

Wenzhao Wu, Juepeng Zheng, Weijia Li, Haohuan Fu, Shuai Yuan, Le Yu

Proceedings Volume 11394, Automatic Target Recognition XXX; 1139406 (2020) https://doi.org/10.1117/12.2557829
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Detection of oil palm tree provides necessary information for monitoring oil palm plantation and predicting palm oil yield. The supervised model, like deep neural network trained by remotely sensed images of the source domain, can obtain high accuracy in the same region. However, the performance will largely degrade if the model is applied to a different target region with another unannotated images, due to changes in relation to sensors, weather conditions, acquisition time, etc. In this paper, we propose a domain adaptation based approach for oil palm detection across two different high-resolution satellite images. With manually labeled samples collected from the source domain and unlabeled samples collected from the target domain, we design a domain-adversarial neural network that is composed of a feature extractor, a class predictor and a domain classifier to learn the domain-invariant representations and classification task simultaneously during training. Detection tasks are conducted in six typical regions of the target domain. Our proposed approach improves accuracy by 25.39% in terms of F1-score in the target domain, and performs 9.04%-15.30% better than existing domain adaptation methods.

Conference Presentation

Citation Download Citation

Wenzhao Wu, Juepeng Zheng, Weijia Li, Haohuan Fu, Shuai Yuan, and Le Yu "Domain adversarial neural network-based oil palm detection using high-resolution satellite images", Proc. SPIE 11394, Automatic Target Recognition XXX, 1139406 (24 April 2020); https://doi.org/10.1117/12.2557829

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available