With the development of deep learning technology, an enormous number of convolutional neural network (CNN) models have been proposed to address the challenging building extraction task from very high-resolution (VHR) remote sensing images. However, searching for better CNN architectures is time-consuming, and the robustness of a new CNN model cannot be guaranteed. In this paper, an improved boundary-aware perceptual (BP) loss is proposed to enhance the building extraction ability of CNN models. The proposed BP loss consists of a loss network and transfer loss functions. The usage of the boundary-aware perceptual loss has two stages. In the training stage, the loss network learns the structural information from circularly transferring between the building mask and the corresponding building boundary. In the refining stage, the learned structural information is embedded into the building extraction models via the transfer loss functions without additional parameters or postprocessing. We verify the effectiveness and efficiency of the proposed BP loss both on the challenging WHU aerial dataset and the INRIA dataset. Substantial performance improvements are observed within two representative CNN architectures: PSPNet and UNet, which are widely used on pixel-wise labelling tasks. With BP loss, UNet with ResNet101 achieves 90.78% and 76.62% on IoU (intersection over union) scores on the WHU aerial dataset and the INRIA dataset, respectively, which are 1.47% and 1.04% higher than those simply trained with the cross-entropy loss function. Additionally, similar improvements (0.64% on the WHU aerial dataset and 1.69% on the INRIA dataset) are also observed on PSPNet, which strongly supports the robustness of the proposed BP loss.

中文翻译：

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从VHR图像中提取建筑物的改进的边界感知知觉损失

随着深度学习技术的发展，已经提出了大量的卷积神经网络（CNN）模型来解决从超高分辨率（VHR）遥感图像中具有挑战性的建筑物提取任务。但是，寻找更好的CNN架构非常耗时，并且无法保证新的CNN模型的鲁棒性。为了提高CNN模型的建筑物提取能力，提出了一种改进的边界感知知觉（BP）损失算法。拟议的BP损失包括损失网络和转移损失功能。边界感知知觉损失的使用分为两个阶段。在训练阶段，损失网络通过在建筑物遮罩和相应建筑物边界之间循环传递来学习结构信息。在炼油阶段 学习到的结构信息通过传递损失函数嵌入到建筑物提取模型中，而无需附加参数或进行后处理。我们在具有挑战性的WHU航空数据集和INRIA数据集上验证了所提出的BP损失的有效性和效率。在两种代表性的CNN架构中观察到了显着的性能改进：PSPNet和UNet，它们被广泛用于像素标记任务。有了BP损失，带有ResNet101的UNet在WHU航空数据集和INRIA数据集的IoU（联合交叉点）得分上分别达到90.78％和76.62％，分别比简单地用交叉熵训练的得分高1.47％和1.04％损失函数。此外，在PSPNet上也观察到了类似的改进（在WHU航空数据集上为0.64％，在INRIA数据集上为1.69％），