Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of interest, this remains a challenging task. In recent years, deep convolutional neural networks have started being used in remote sensing applications and demonstrate state of the art performance for pixel level classification of objects. Here we propose a reliable framework for performant results for the task of semantic segmentation of monotemporal very high resolution aerial images. Our framework consists of a novel deep learning architecture, ResUNet-a, and a novel loss function based on the Dice loss. ResUNet-a uses a UNet encoder/decoder backbone, in combination with residual connections, atrous convolutions, pyramid scene parsing pooling and multi-tasking inference. ResUNet-a infers sequentially the boundary of the objects, the distance transform of the segmentation mask, the segmentation mask and a colored reconstruction of the input. Each of the tasks is conditioned on the inference of the previous ones, thus establishing a conditioned relationship between the various tasks, as this is described through the architecture’s computation graph. We analyse the performance of several flavours of the Generalized Dice loss for semantic segmentation, and we introduce a novel variant loss function for semantic segmentation of objects that has excellent convergence properties and behaves well even under the presence of highly imbalanced classes. The performance of our modeling framework is evaluated on the ISPRS 2D Potsdam dataset. Results show state-of-the-art performance with an average F1 score of 92.9% over all classes for our best model.

对于各种遥感应用中的自动监视任务，对高分辨率航拍图像的场景理解至关重要。由于关注对象的像素值在类内和类间的差异较大，因此这仍然是一项艰巨的任务。近年来，深度卷积神经网络已开始在遥感应用中使用，并展示了对象像素级分类的最新技术性能。在这里，我们为单时间超高分辨率航拍图像的语义分割任务提供了一种可靠的，可用于性能结果的框架。我们的框架包括一个新颖的深度学习体系结构ResUNet-a和一个基于Dice损失的新颖损失函数。ResUNet-a将UNet编码器/解码器主干与残差连接，无用卷积，金字塔场景解析池和多任务推理结合使用。ResUNet-a依次推断对象的边界，分割蒙版的距离变换，分割蒙版和输入的彩色重建。每个任务都以先前任务的推论为条件，从而在各种任务之间建立条件关系，正如通过体系结构的计算图所描述的那样。我们分析了几种广义骰子损失的语义分割性能，并为对象的语义分割引入了一种新颖的变体损失函数，该函数具有出色的收敛性，即使在高度不平衡的类下也表现良好。我们的建模框架的性能在ISPRS 2D Potsdam数据集上进行了评估。结果显示了最先进的性能，F1平均得分为92。