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.
The effective detection of urban development is the basis of understanding urban sustainability. Although various studies concentrated on long-time-series analysis on urban development, the resolution of images was too low to focus on a single object. In this paper, we provide a long-time-series analysis of built-up areas at an annual frequency in Beijing, China, from 2000 to 2015, based on the automatic building extraction and high-resolution satellite images. We propose a deeplearning based method to extract buildings, and employ an ensemble learning method to improve the localization of boundaries. The time-series results of built-up areas are analyzed based on two schemes, i.e., change detection over the past fifteen years and evaluation of the whole region in three selected years. Our proposed method achieves an average overall accuracy (OA) of 93%. The results reveal that Beijing developed more rapidly during 2001-2008 than other periods in terms of the density and the number of buildings.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.