Deep learning-based active contour model for remote sensing building image segmentation

Remote sensing building image segmentation faces complex building boundary and shape challenges due to the underutilisation of local features and global geometric information, as well as the dependence on initial contours. To overcome these problems, this paper proposes an end-to-end modelling framework called ACM-ResNet, which integrates the levelset Active Contour Model (ACM) and the ResNet deep learning network. ACM-ResNet utilises evolving level-set functions to progressively optimise the accuracy and smoothness of the contours, thus effectively addressing complex building boundary and shape challenges. Most importantly, ACM-ResNet no longer relies on manually labelling or setting the initial contour, but adaptively adjusts the contour position and shape starting from the predicted initial contour, improving the automation and robustness of the segmentation process. We evaluate our segmentation model on the Vaihingen and Bing Huts datasets, and the experimental results show that our method outperforms competing methods in terms of performance.