Abstract
In order to extract targets in different scale under complex scene, this paper proposes a remote sensing image segmentation model based on Conditional Generative Adversarial Network (CGAN) combing multi-scale contextual information. This end-to-end model consists of a generative network and a discriminant network. A SegNet model fusing multi-scale contextual information is proposed as the generative network. In order to extract multi-scale contextual information, the multi-scale features of the end pooling feature map in the encoder are extracted using different proportion of dilated convolution. The multi-scale features are further fused with the global feature. The discriminant network is a convolution neural network for two category classification, determines whether the input is a generated result or the ground truth. After alternate adversarial training, the experimental results on a remote sensing road dataset show that the road segmentation results of the proposed model are superior to those of the comparable models in terms of target integrity and details preserving.
This work is supported by the Second Tibetan Plateau Scientific Expedition and Research (2019QZKK0405).
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Wang, X., Zhang, X., Guo, H., Yu, S., Zhang, S. (2020). A Remote Sensing Image Segmentation Model Based on CGAN Combining Multi-scale Contextual Information. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_30
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DOI: https://doi.org/10.1007/978-3-030-60633-6_30
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