论文标题
Budd:多模式贝叶斯更新森林砍伐探测
BUDD: Multi-modal Bayesian Updating Deforestation Detections
论文作者
论文摘要
森林退化的全球现象是一个紧迫的问题,对气候稳定和生物多样性保护有严重影响。在这项工作中,我们通过将Sentinel-1反向散射并与Sentinel-2归一化植被指数数据结合在一起,生成贝叶斯更新森林砍伐检测(BUDD)算法。我们表明该算法在验证AOI中提供了良好的性能。我们将三种数据模式的不同组合的有效性与BUDD算法的输入进行了比较,并根据光学图像进行了与现有基准测试的有效性。
The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-1 backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.