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

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.