Abstract
In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different advantages and disadvantages. But in the field of remote sensing, many scholars usually do comparison between DCNN models and traditional machine learning. In this paper, we compare different state-of-the-art DCNN models mainly over two publicly available remote sensing datasets—airplane dataset and car dataset. Such comparison can provide guidance for related researchers. Besides,we provide suggestions for fine-tuning different DCNN models. Moreover, for DCNN models including fully connected layers, we provide a method to save storage space.
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Acknowledgements
This work was supported by the project of National Science Fund for Distinguished Young Scholars of China (Grant No. 60902067) and the Key Science-Technology Project of Jilin Province (Grant No. 11ZDGG001).
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Ding, P., Zhang, Y., Jia, P. et al. A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images. Neural Process Lett 49, 1369–1379 (2019). https://doi.org/10.1007/s11063-018-9878-5
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DOI: https://doi.org/10.1007/s11063-018-9878-5