TY - GEN
T1 - Woodland Detection Using Most-Sure Strategy to Fuse Segmentation Results of Deep Learning
AU - Gui, Yuanyuan
AU - Li, Wei
AU - Wang, Yanan
AU - Yue, Anzhi
AU - Pu, Ying
AU - Chen, Xinyun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - For obtaining information about ecosystem resource, GF-1 satellite was launched on April 26, 2013, which is the first satellite of the China's High-Resolution Earth Observation System. After obtaining some of the remote sensing images from GF-1, we selected WFV(wide field vision) images and detected the woodland to separate it from other geography types in the images. First, WFV images were clipped and labeled, then two deep learning models, POI-Net and Deep-UNet were used for training. We fused the prediction matrixes of deep learning networks using proposed "Most-sure strategy". The results show that our method can effectively improve the accuracy of woodland detection and segmentation results are outstanding. In addition, the proposed framework can also detect woodland in images returned by GF-6 satellite.
AB - For obtaining information about ecosystem resource, GF-1 satellite was launched on April 26, 2013, which is the first satellite of the China's High-Resolution Earth Observation System. After obtaining some of the remote sensing images from GF-1, we selected WFV(wide field vision) images and detected the woodland to separate it from other geography types in the images. First, WFV images were clipped and labeled, then two deep learning models, POI-Net and Deep-UNet were used for training. We fused the prediction matrixes of deep learning networks using proposed "Most-sure strategy". The results show that our method can effectively improve the accuracy of woodland detection and segmentation results are outstanding. In addition, the proposed framework can also detect woodland in images returned by GF-6 satellite.
KW - Deep Learning
KW - Most-sure Fusion Strategy
KW - Remote Sensing Image
KW - Woodland Detect
UR - http://www.scopus.com/inward/record.url?scp=85077675301&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8897705
DO - 10.1109/IGARSS.2019.8897705
M3 - Conference contribution
AN - SCOPUS:85077675301
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6724
EP - 6727
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -