论文标题
使用深暹罗卷积神经网络检测城市动态
Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks
论文作者
论文摘要
变更检测是计算机视觉和遥感领域中快速增长的纪律。在这项工作中，我们设计和开发了一种卷积神经网络（CNN）的变体，称为暹罗CNN，从不同时间捕获的Mekelle City的Sentinel-2时间图像中提取特征，并检测因城市化而引起的变化：建筑物和道路。根据总体准确性（95.8），Kappa度量（72.5），召回（76.5），精度（77.7），F1度量（77.1）来衡量所提出的检测能力。该模型在大多数这些措施方面都取得了良好的表现，可用于检测Mekelle和其他城市的变化，并在不同的时间范围内进行城市化。
Change detection is a fast-growing discipline in the areas of computer vision and remote sensing. In this work, we designed and developed a variant of convolutional neural network (CNN), known as Siamese CNN to extract features from pairs of Sentinel-2 temporal images of Mekelle city captured at different times and detect changes due to urbanization: buildings and roads. The detection capability of the proposed was measured in terms of overall accuracy (95.8), Kappa measure (72.5), recall (76.5), precision (77.7), F1 measure (77.1). The model has achieved a good performance in terms of most of these measures and can be used to detect changes in Mekelle and other cities at different time horizons undergoing urbanization.
