Remote Sensing Image Recognition Method Based on Faster R-CNN

Chao Ma, Jinzhao Li, Zecong Wang, Xianyong Yi, Linyi Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publication2020 International Conference on Computer Engineering and Application (ICCEA)
Number of pages4
ISBN (Print)9781728159041
StatePublished - May 30 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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