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深度学习的快速发展,为高分辨率卫星遥感影像解译提供了更好的技术手段和应用前景。围绕高分卫星遥感影像地表覆盖信息提取,利用高分辨率卫星遥感影像制作5种常见的地表覆盖类型的像素级样本数据集,并提出一种基于注意力增强与多尺度特征融合的语义分割方法,实现地表覆盖自动提取。通过影像波段选择、预训练模型迁移学习、损失函数改进等方法,提升语义分割模型识别精度,最优的提取结果中,5种地表覆盖类型的F
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均值、IoU均值和总体精度分别达到了78.6%、66.8%、85.0%,除道路之外,耕草、林地、建筑、水体的F
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均超过80%,且分类图斑边界能够与影像中的地物边界很好套合。实验表明建立的卫星影像地表覆盖分类样本数据集和分类方法,能够应用于高分辨率卫星影像地表覆盖信息提取。
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