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首页> 外文会议>European Conference on Synthetic Aperture Radar >High-resolution SAR image large area built-up extraction based on the improved BN U-Net – a case study of the North China Plain
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High-resolution SAR image large area built-up extraction based on the improved BN U-Net – a case study of the North China Plain

机译：基于改进的BN U-Net的高分辨率SAR图像大面积建筑提取 - 以华北平原为例

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摘要

Automatic extraction of built-up area from high resolution SAR images is still a challenging problem due to the complexity of buildings’ backscattering. In high resolution SAR images, the buildings have complicated texture and structural features under different topographic conditions. Thus, how to make fine description and accurate classification is a very worthwhile problem. To this end, we proposed an improved BN U-Net network model with migration learning strategy for built-up area extraction in large area and complex environment. The Dice Loss function was used to replace the cross entropy Loss function to solve the imbalance of the building distribution. Experimental results on GF3-FS II SAR dataset demonstrate the effectiveness of the proposed fusion scheme.
机译：由于建筑物的反向散射的复杂性，从高分辨率SAR图像自动提取来自高分辨率SAR图像的内置区域仍然是一个具有挑战性的问题。 在高分辨率SAR图像中，建筑物在不同的地形条件下具有复杂的质地和结构特征。 因此，如何进行精细描述和准确的分类是一个非常有价值的问题。 为此，我们提出了一种改进的BN U-Net网络模型，具有大面积和复杂环境中的内置区域提取的迁移学习策略。 骰子损失函数用于更换跨熵损耗功能以解决建筑分布的不平衡。 GF3-FS II SAR Dataset上的实验结果证明了拟议融合方案的有效性。

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