Abstract
Automatically detecting ground object from optical remote sensing images has attracted significant attention due to its importance in both military and civilian fields. However, the diversity of configuration for different object and the complex background information makes this task difficult. Moreover, the high-level semantic information is usually ignored. To address these problems, we propose an efficient method that extracts deep feature with high-level semantic information from a classification convolutional neural network, and separates the regions of interested based on deep feature. Then each region of interest will be sent to another convolutional neural network to verify whether they are true objects or not. Our proposed method can adapt different objects. Also, it doesn’t need any bounding box information for training. We build two remote sensing datasets, SROD-3 and RSHOA-4, to evaluate our detection method. Experiment result indicates that our detection method performs better than other state of the art methods, including Faster-RCNN and YOLO9000.
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Acknowledgments
The authors would like to thank all the researchers and community who gladly shared the open source codes and tools used in this paper. And this work is supported by the National Natural Science Foundation of China (Grant No. 61272317).
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Zhao, X., Wan, S., Zou, C., Li, X., Yan, L. (2018). Object Detection Based on Deep Feature for Optical Remote Sensing Images. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_35
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DOI: https://doi.org/10.1007/978-981-13-0893-2_35
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