Deep Learning for Semantic Segmentation of UAV Videos

Yiwen Wang, Ye Lyu, Yanpeng Cao, Michael Ying Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherIEEE
Pages2459-2462
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - 28 Jul 2019
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Abbreviated titleIGARSS 2019
CountryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • Conv-LSTM
  • FCN
  • UAV
  • video semantic segmentation

Fingerprint Dive into the research topics of 'Deep Learning for Semantic Segmentation of UAV Videos'. Together they form a unique fingerprint.

  • Cite this

    Wang, Y., Lyu, Y., Cao, Y., & Yang, M. Y. (2019). Deep Learning for Semantic Segmentation of UAV Videos. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 2459-2462). [8899786] (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. https://doi.org/10.1109/IGARSS.2019.8899786