【摘要】 针对传统基于光谱和面向对象的建筑物提取方法多噪声和边缘羽化严重,标准U-Net高分遥感影像解译计算开销大等问题,提出了一种改进方法。该方法通过修改标准U-Net输入样本尺寸、卷积核数量和卷积层数量,基于Adam最优化算法,采用逐维度加和特征融合取代沿通道维度联合特征融合,并首次将空间置弃层引入标准U-Net中用于提高模型效率和改善目标边缘精度。定量分析和实验结果表明:空间置弃层有助于缓解因数据特征分布不均衡导致的精度变异问题;融合空间置弃层的改进U-Net模型可以有效改善椒盐噪声和建筑物边缘羽化问题,提高面向复杂场景高分遥感影像解译精度,加快模型训练收敛速度,减少模型参数数量和模型训练时间。更多还原

【Abstract】 An improved method is proposed to cut the high computation cost of a classic U-Net for high-resolution image interpretation,meanwhile,to subdue salt-and-pepper noises and edge feathering generated from building extraction by spectralbased and object-oriented methods,respectively.The size of input images and the number of convolution kernels and convolution layers of the classic U-Net are modified and spatial dropout layers are innovatively integrated for the first time to improve modeling efficiency and edge accuracy in the proposed method derived from the Adam algorithm,adopting dimensionwise instead of channel-wise feature fusion.Results of quantitative analysis and experiments show that spatial dropout layers are beneficial to alleviate accuracy variation caused by an uneven distribution of data features.The proposed U-Net integrating spatial dropout layers can effectively restrain salt-pepper noises and edge feathering,raise the overall accuracy of high-resolution image interpretation for complex scenes,accelerate training and reduce the number of parameters and elapsed time.更多还原