Abstract
The object detection algorithm can accurately detect the objects needed in remote sensing images. For the traditional object detection which adopts the sliding window method, both the detection accuracy and robustness are low and the generalization ability is weak. It is difficult to meet people’s demand for production and application. With the establishment of large-scale image database, the development of deep convolutional network has been promoted, which greatly improves the accuracy of object detection in images. In the field of target object detection, the algorithm based on depth of deep learning is the most popular and effective ones. Through a large number of experiments on the NWPU VHR-10 data set, we verified the remote sensing image detection based on FasterRCNN. The experiment results showed that the average accuracy of the ten types of targets in the NWPU VHR-10 data set reached 81.59%, which therefore proved the effectiveness of deep learning algorithm in remote sensing image detection.
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Acknowledgements
This work was supported in part by the Tianshan Young Talent Program, Xinjiang Uygur Autonomous Region under Grant 2018Q024, in part by the Natural Science Foundation of China under Grant 61771089 and Grant 61961040, and in part by the Regional Cooperative Innovation Program of Autonomous Region (Aid Program of Science and Technology to Xinjiang) under Grant 2019E0214.
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Liu, S., Ma, Z., Chen, B. (2021). Remote Sensing Image Detection Based on FasterRCNN. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z., Cai, X. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 653. Springer, Singapore. https://doi.org/10.1007/978-981-15-8599-9_44
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DOI: https://doi.org/10.1007/978-981-15-8599-9_44
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