Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network

ABSTRACT

In this study, Sentinel-2 optical satellite imagery was acquired over the Peace Athabasca Delta and assessed for its open water classification capabilities using an object-oriented  deep learning algorithm . The workflow involved segmenting the satellite data into meaningful image objects, building a Convolutional Neural Network (CNN), training the CNN, and lastly applying the CNN, resulting in probability heat maps of open water (with score values ranging from 0–1). Using the vector segmentation, heat maps were then iteratively assigned final class labels (‘open water’ or ‘other’) based on various probability thresholding. The ensuing open water classifications were assessed against a large validation dataset, and a highest overall accuracy of 96.2% (0.912 kappa coefficient) was achieved, with an open water producer’s accuracy of 98.1%. These results were then compared against a Random Forest (RF) classification, and results indicated that the CNN algorithm outperforms RF in this study site. Additionally, an important component of this study was the optimization of several CNN configurations, including patch size and learning rate; the latter which plays a critical role in model adaptation. The optimized object-oriented CNN and associated results can be used to provide resource managers with accurate surface water extent maps at 10 m resolution.

Acknowledgments

The author, and Ducks Unlimited Canada, would like to thank the North American Wetlands Conservation Act (NAWCA) conservation program for providing funds to our boreal program to complete projects like these, which aim to support the long-term protection of wetlands. A special thanks is also extended to the anonymous reviewers for their useful and constructive feedback.

Declaration of interest statement

The author declares no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, M.A.M, upon reasonable request.