WOODLAND SEGMENTATION OF GAOFEN-6 REMOTE SENSING IMAGES BASED ON DEEP LEARNING

Yuanyuan Gui, Wei Li*, Mengmeng Zhang, Anzhi Yue

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Gaofen-6 (GF-6) is a geostationary, earth-observation satellite, rely on it’s multi-spectral images, GF-6 has the ability to support the monitoring of woodland resources. In this paper, the multi-spectral images sent by GF-6 are studied as dataset, and a model called Infrared Attention Network (InfAttNet) which based on semantic segmentation method is proposed to distinguish woodland from other land types to achieve the purpose of woodland extraction. To make full use of the spectral information, InfAttNet has an additional encoder to extract the features of infrared bands independently. Besides, infrared attention blocks help InfAttNet to enhance the characteristics of woodland. The experimental results proved that InfAttNet improves the accuracy of woodland extraction, and the segmentation effect is strengthened compared with classical networks.

Original languageEnglish
Pages5409-5412
Number of pages4
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Keywords

  • Attention Block
  • Deep Learning
  • Infrared Spectrums
  • Remote Sensing Image
  • Woodland Segmentation

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Gui, Y., Li, W., Zhang, M., & Yue, A. (2021). WOODLAND SEGMENTATION OF GAOFEN-6 REMOTE SENSING IMAGES BASED ON DEEP LEARNING. 5409-5412. Paper presented at 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium. https://doi.org/10.1109/IGARSS47720.2021.9554398