Abstract
Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net.
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Acknowledgements
This work is partially supported by National Science Foundation of China (61972187), Natural Science Foundation of Fujian Province (2020J01828, 2020J02024, 2022J011112, 2020J01826), Fuzhou Science and Technology Major Project (2022FZZD0112), Fuzhou Technology Planning Program (2021-ZD-284), and the Open Program of The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University (KLCCIIP2020202).
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Wu, J., Cai, Y., Wang, T., Luo, Z., Shan, S., Li, Z. (2024). A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_34
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DOI: https://doi.org/10.1007/978-981-99-9637-7_34
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