Buildings' segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, Scale-invariant Neural Network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and Dense Atrous Spatial Pyramid Pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state of the art models on the Open Cities AI and the Multi-Scale Building datasets with a steady improvement margin across different spatial resolutions.
翻译:建筑分割是地球观测和航空图像分析领域的一项基本任务。文献中现有的大部分深层学习方法可以适用于固定或窄距离空间分辨率图像。在实际情景中,用户处理的图像分辨率范围很广。因此,给定的航空图像往往需要重新取样,以与用于培训深层学习模型的数据集的空间分辨率相匹配,从而导致分离性功能的退化。为了克服这一挑战,我们在本手稿中提议,规模变化性神经网络(Sci-Net)结构由宽距离空间分辨率空中图像组成的建筑组成。具体地说,我们的方法是利用UNet的等级代表制和Dense Atrous空间卫星集,以提取精细度的多尺度表示式。Sci-Net明显地超越了开放城市AI和多层建筑数据集的艺术模型的状态,在不同空间分辨率上稳步改进空间空间空间空间空间空间空间空间空间比值。