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 language | English |
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Pages | 5409-5412 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 |
Conference
Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
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Country/Territory | Belgium |
City | Brussels |
Period | 12/07/21 → 16/07/21 |
Keywords
- Attention Block
- Deep Learning
- Infrared Spectrums
- Remote Sensing Image
- Woodland Segmentation