DARNet: Deep Active Ray Network for Building Segmentation
This addresses building segmentation for applications like urban planning, but it is incremental as it builds on existing active contour and CNN methods.
The paper tackles building segmentation by proposing DARNet, which uses a deep CNN to predict energy maps and evolves a polygon-based contour in polar coordinates to minimize energy, achieving state-of-the-art or comparable performance on three datasets.
In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. A polygon-based contour is then evolved via minimizing the energy function, of which the minimum defines the final segmentation. Instead of parameterizing the contour using Euclidean coordinates, we adopt polar coordinates, i.e., rays, which not only prevents self-intersection but also simplifies the design of the energy function. Moreover, we propose a loss function that directly encourages the contours to match building boundaries. Our DARNet is trained end-to-end by back-propagating through the energy minimization and the backbone CNN, which makes the CNN adapt to the dynamics of the contour evolution. Experiments on three building instance segmentation datasets demonstrate our DARNet achieves either state-of-the-art or comparable performances to other competitors.