The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task, which requires innovative methods to guarantee optimal results with unquestionable value and within reasonable time. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results, which can be further refined and used in a post-processing workflow for a large variety of applications.
翻译:过去几年来,遥感领域对变化探测的兴趣有所增加,搜索卫星图像的变化有许多有益的应用,从土地覆盖和土地使用分析到异常点探测,特别是城市变化探测为研究城市传播和增长提供了有效工具,通过几年的观测,同时,变化探测往往具有计算上的挑战性和耗时性,需要创新方法,保证在合理时间内在不可置疑的价值下取得最佳结果。在本文件中,我们提出了两种不同的变化探测方法(语法分割和分类),这两种方法都利用了超导神经网络,以取得良好结果,可以进一步完善,并用于大量应用的后处理工作流程。