Deep Learning – A new approach for multi-label scene classification in Planetscope and Sentinel-2 imagery

Dr Yuri Shendryk1, Yannik Rist1, Dr Catherine Ticehurst2, Dr Peter Thorburn1

1CSIRO, Brisbane, Australia,

2CSIRO, Canberra, Australia

 

Motivated by the increasing availability of high-resolution satellite imagery, we developed deep learning models able to efficiently and accurately classify the atmospheric conditions and dominant classes of land cover/land use in commercial PlanetScope imagery acquired over the Amazon rainforest. In specific, we trained deep convolutional neural network (CNN) to perform multi-label scene classification of high-resolution (<10 m) satellite imagery. We also discuss the challenges and opportunities in training ensemble CNN models for multi-label scene classification. Our best performing model achieved an F-beta score of 0.91, which was only 2% short of the top performing model in the Understanding the Amazon from Space Kaggle competition. Finally, we investigate the transferability of our PlanetScope-trained models to freely available Sentinel-2 imagery acquired over the wet tropics of Australia. The models trained on PlanetScope imagery performed well when applied to Sentinel-2 imagery with F-beta score of 0.79. Similarly, the models trained on Sentinel-2 imagery achieved F-beta score of 0.80 when applied to PlanetScope imagery. This suggests that our CNN models are suitable for classifying the atmospheric conditions and dominant classes of land cover/land use in satellite imagery of similar resolution to that of PlanetScope and Sentinel-2 (i.e.  <10 m).


Biography:

Dr Yuri Shendryk is a Postdoctoral Fellow at CSIRO specializing in developing algorithms to process terabytes of satellite and airborne data. After earning a master’s degree in geophysics, he spent more than three years working and studying geospatial engineering in Sweden and Germany. In 2017 he received a PhD from UNSW, and his current research in CSIRO is centred around the integration of remote sensing and machine learning for forest health monitoring and precision agriculture.

ABOUT AeRO

AeRO is the industry association focused on eResearch in Australasia. We play a critical coordination role for our members, who are actively transforming research via Information Technology. Organisations join AeRO to advance their own capabilities and services, to collaborate and to network with peers. AeRO believes researchers and the sector significantly benefit from greater communication, coordination and sharing among the increasingly different and evolving service providers.

Conference Managers

Please contact the team at Conference Design with any questions regarding the conference.
© 2017 Conference Design Pty Ltd