In this work, we present a multi-view framework to classify spatio-temporal phenomena at multiple resolutions. This approach utilizes the complementarity of features across different resolutions and improves the corresponding models by enforcing consistency of their predictions on unlabeled data. Unlike traditional multi-view learning problems, the key challenge in our case is that there is a many-to-one correspondence between instances across different resolutions, which needs to be explicitly modeled. Experiments on the real-world application of mapping urban areas using spatial raster data-sets from satellite observations show the benefits of the proposed multi-view framework.
|Original language||English (US)|
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Dec 2019|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: Dec 9 2019 → Dec 12 2019
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||12/9/19 → 12/12/19|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENT This research was supported by National Science Foundation under Grants IIS-1838159. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
© 2019 IEEE.
- multi-instance learning
- multi-resolution classification
- remote sensing