Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery, providing problem-specific energy potentials. We validated our approach on 3 real-world scenes of varying complexity, using 730 manually labeled polygon outlines as ground truth. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection baseline was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a high-quality semantic segmentation method (e.g. U-net) as the basis for individual stem detection, as the quality of the results degraded dramatically in our baseline experiment utilizing a simpler method. Our method is a step towards increased accessibility of automatic fallen tree mapping in forests, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems.

在过去几年中，基于深度神经网络的语义图像分割取得了很大进展。另一方面，单实例分割仍然是一个具有挑战性的问题。在本文中，我们介绍了一个框架，用于通过对通过完全卷积网络获得的图像的语义分割图进行多重主动轮廓演化来分割常见对象类的实例。轮廓演化被视为能量最小化问题，其中聚合能量函数包含数据拟合项、显式形状模型并考虑对象重叠。提出了有效的解决方案邻域算子，通过模拟退火等元启发式方法实现优化。我们在分割来自高分辨率航空多光谱图像的单个坠落物的背景下实例化了所提出的框架，提供了特定于问题的能量潜力。我们使用 730 个手动标记的多边形轮廓作为基本事实，在 3 个不同复杂度的真实世界场景中验证了我们的方法。试验地位于德国巴伐利亚森林国家公园的地区，那里遭受了严重的树皮甲虫侵扰。在多边形和线段级别上进行了评估，表明多轮廓分割可以实现高达 0.93 的精度和 0.82 的召回率。与迭代样本一致线段检测基线相比，召回率提高了 7 个百分点 (pp)，精度提高了 6 个百分点。尽管应用的形状参数化很简单，纳入能量函数的显式形状模型将结果提高了多达 4 pp 的召回率。最后，我们展示了使用高质量语义分割方法（例如 U-net）作为单个词干检测基础的重要性，因为在使用更简单方法的基线实验中，结果质量急剧下降。由于与激光扫描相比，航空影像采集的成本效率更高，我们的方法是提高森林中自动倒树测绘的可访问性的一步。精确的倒下树木地图可进一步用作动植物栖息地建模、碳固存研究以及森林生态系统土壤质量的基础。我们展示了使用高质量语义分割方法（例如 U-net）作为单个词干检测基础的重要性，因为在使用更简单方法的基线实验中，结果质量急剧下降。由于与激光扫描相比，航空影像采集的成本效率更高，我们的方法是提高森林中自动倒树测绘的可访问性的一步。精确的倒下树木地图可进一步用作动植物栖息地建模、碳固存研究以及森林生态系统土壤质量的基础。我们展示了使用高质量语义分割方法（例如 U-net）作为单个词干检测基础的重要性，因为在使用更简单方法的基线实验中，结果质量急剧下降。由于与激光扫描相比，航空影像采集的成本效率更高，我们的方法是提高森林中自动倒树测绘的可访问性的一步。精确的倒下树木地图可进一步用作动植物栖息地建模、碳固存研究以及森林生态系统土壤质量的基础。由于与激光扫描相比，航空影像采集的成本效率更高，我们的方法是提高森林中自动倒树测绘的可访问性的一步。精确的倒下树木地图可进一步用作动植物栖息地建模、碳固存研究以及森林生态系统土壤质量的基础。由于与激光扫描相比，航空影像采集的成本效率更高，我们的方法是提高森林中自动倒树测绘的可访问性的一步。精确的倒下树木地图可进一步用作动植物栖息地建模、碳固存研究以及森林生态系统土壤质量的基础。