Abstract
Effective analysis of remote sensing images remains a challenging topic in deep learning research field. This paper proposes a semantic segmentation approach based on the inception architecture. Specifically, the combination of residual network and Inception improves the feature extraction capability of encoder network. In addition, this paper discusses the problem of unbalanced information allocation caused by concatenated operation in the up-sampling process of network models such as Unet, and the RCSE module is proposed to complete feature reconstruction to solve this problem. The approach ensures accurate assignment of semantic labels to the buildings in the aerial images. Experiments based on the dataset proposed by CrowdAi certify the effectiveness of our approach with a 3.7% IoU improvement compared to Unet.
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Acknowledgement
This research was supported by [2018GZ0517] [2019YFS0146] [2019YFS0155] which supported by Sichuan Provincial Science and Technology Department, [2018KF003] Supported by State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province [2017B010110007]. Project S201910619048 supported by Sichuan’s Training Program of Innovation and Entrepreneurship for Undergraduate.
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Xu, K. et al. (2019). IRSNET: An Inception-Resnet Feature Reconstruction Model for Building Segmentation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_5
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DOI: https://doi.org/10.1007/978-3-030-36802-9_5
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