This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%.

翻译：本文提出了一种名为Deep Networks for Improved Segmentation Edges（DeNISE）的新型数据增强技术，该技术利用边缘检测与分割模型来提升分割掩膜的边界质量。DeNISE通过两个连续深度神经网络架构的内在差异提高预测分割边缘的精度。该方法适用于所有类型的神经网络，且无需端到端训练，从而能够快速实验以发现模型间的互补性。我们针对航拍图像中的建筑物分割任务对DeNISE进行了测试与应用。航拍图像因分辨率较低且存在反射、阴影及视觉遮挡等光学噪声而具有较大处理难度。总体而言，本文展示了DeNISE的潜力。应用该技术后，我们在建筑物分割任务上的基线结果得到提升，实现了78.9%的建筑物IoU。