TY - GEN
T1 - Remote Sensing Image Recognition Method Based on Faster R-CNN
AU - Ma, Chao
AU - Li, Jinzhao
AU - Wang, Zecong
AU - Yi, Xianyong
AU - Li, Linyi
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2020/5/30
Y1 - 2020/5/30
N2 - This paper proposes a method for remote sensing image recognition based on Faster R-CNN. Using Faster R-CNN model and ZFNet as the basic network, experiments show that the accuracy rate of Architecture, Greenhouses and Paddy field recognition is 90.67%, 93.85%, 83.33%, and the average recognition accuracy reached 89.28%. At the same time, compared with the recognition results of recognition detection methods such as CNN and TT-RICNN, it was found that the proposed Faster R-CNN model has better recognition performance well, with good recognition detection accuracy.
AB - This paper proposes a method for remote sensing image recognition based on Faster R-CNN. Using Faster R-CNN model and ZFNet as the basic network, experiments show that the accuracy rate of Architecture, Greenhouses and Paddy field recognition is 90.67%, 93.85%, 83.33%, and the average recognition accuracy reached 89.28%. At the same time, compared with the recognition results of recognition detection methods such as CNN and TT-RICNN, it was found that the proposed Faster R-CNN model has better recognition performance well, with good recognition detection accuracy.
UR - http://hdl.handle.net/10754/663837
UR - https://ieeexplore.ieee.org/document/9103836/
UR - http://www.scopus.com/inward/record.url?scp=85086409175&partnerID=8YFLogxK
U2 - 10.1109/ICCEA50009.2020.00191
DO - 10.1109/ICCEA50009.2020.00191
M3 - Conference contribution
SN - 9781728159041
SP - 869
EP - 872
BT - 2020 International Conference on Computer Engineering and Application (ICCEA)
PB - IEEE
ER -