论文标题
可自动化图像分割的端到端可训练的深度主动轮廓模型：空中图像中的划定建筑物
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
论文作者
论文摘要
遥感图像中建筑物的自动分割是一项具有挑战性的任务，需要在通常的大图像区域上准确描述多个建筑实例。手动方法通常是费力的，目前基于深度学习的方法无法描绘所有建筑实例，并以足够的准确性来做到这一点。作为解决方案，我们提出了可训练的深活动轮廓（TDAC），这是一个自动图像分割框架，该框架与卷积神经网络（CNN）和主动轮廓模型（ACMS）密切合并。 ACM组件的Eulerian Energy函数包括由Backbone CNN预测的每个像素参数图，该图也初始化了ACM。重要的是，ACM和CNN组件都在TensorFlow中完全实现，并且整个TDAC体系结构都是端到端自动可区分的，并且可以在无需用户干预的情况下训练。 TDAC可以快速，准确且全自动同时划定图像中的许多建筑物。我们在两个公开可用的航空图像数据集上验证了该模型以进行构建细分，我们的结果表明TDAC建立了新的最新性能。
The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.
