Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~(SAR) data. Our proposed method is a probabilistic model trained with unsupervised learning techniques, reconstruction, and contrastive learning. The change map is generated with the help of the distribution difference between pre-incident and post-incident data. Our proposed CD model is evaluated on flood detection data. We verified the efficacy of our model on 8 different flood sites, including three recent flood events from Copernicus Emergency Management Services and six from the Sen1Floods11 dataset. Our proposed model achieved an average of 64.53\% Intersection Over Union(IoU) value and 75.43\% F1 score. Our achieved IoU score is approximately 6-27\% and F1 score is approximately 7-22\% better than the compared unsupervised and supervised existing CD methods. The results and extensive discussion presented in the study show the effectiveness of the proposed unsupervised CD method.
翻译:人类文明对地球系统的影响越来越强大。受气候变化和土地使用变化的影响,洪水等自然灾害近年来一直在增加。地球观测是评估和减轻负面影响的宝贵来源。从地球观测数据中检测变化是监测潜在影响的一种方法。有效和可靠的变化探测方法有助于在早期确定灾害事件风险。在这项工作中,我们提议在时间序列合成孔径雷达~(SAR)数据中采用新的不受监督的CD方法。我们提议的方法是用不受监督的学习技术、重建以及对比性学习来培训的概率模型。利用事件前和事件后数据之间的分布差异来帮助绘制变化图。我们提议的CD模型在洪水探测数据上进行了评估。我们核实了8个不同洪水地点的模式的功效,包括最近从Copernicus应急管理服务处提出的3次洪水事件,以及从Sen1Floods11数据集提出的6。我们提议的模型实现了平均的64.53 ⁇ 跨联盟间讨论、重建和对比对比性学习技术、75.43\_F等级的评分,我们目前的CD评分的CD-22等级方法大约比目前的CD评分的CD-22级的CD评分。