Wildfires are increasing in intensity and frequency across the globe due to climate change and rising global temperature. Development of novel approach to Monitor wildfire progressions in near real-time is therefore of critical importance for emergency responses. The objective of this research is to investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR and Sentinel-2 MSI time series for increasing the frequency and accuracy of wildfire progression mapping. In this study, optical-based burned areas prior to each SAR acquisition (when available) were accumulated into SAR-based pseudo progression masks to train a deep residual U-Net model. Unlike multi-temporal fusion of SAR and optical data, the temporal fusion of progression masks allows us to track as many wildfire progressions as possible. Specifically, two approaches were investigated to train the deep residual U-Net model for continuous learning: 1) Continuous joint training (CJT) with all historical data (including both SAR and optical data); 2) Learning without forgetting (LwF) based on newly incoming data alone (SAR or optical). For LwF, a mean squared loss was integrated to keep the capabilities learned before and prevent it from overfitting to newly incoming data only. By fusing optical-based burned areas, SAR-based progression pseudo masks improve significantly, which benefits both data sampling and model training, considering the challenges in SAR-based change extraction attributed to the variability in SAR backscatter of the surrounding environments. Pre-trained ResNet was frozen as the encoder of the U-Net model, and the decoder part was trained to further refine the derived burned area maps in a progression-wise manner. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019–2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also observed that the SAR cross-polarization ratio (VH/VV) shows good capability in suppressing multiplicative noise and detecting burned areas when VH and VV have diverse temporal behaviors.

由于气候变化和全球温​​度升高，全球范围内的野火强度和频率正在增加。因此，开发新的方法以近乎实时地监测野火的进展对于应急响应至关重要。这项研究的目的是通过利用Sentinel-1 SAR和Sentinel-2 MSI时间序列来研究U-Net的持续学习，以提高野火进行制图的频率和准确性。在这项研究中，在每次SAR采集之前（如果可用），将基于光学的燃烧区域累积到基于SAR的伪级数掩码中，以训练深层残余U-Net模型。与SAR和光学数据的多时间融合不同，渐进面罩的时间融合使我们能够跟踪尽可能多的野火进展。具体来说，研究了两种方法来训练深层残差U-Net模型以进行连续学习：1）使用所有历史数据（包括SAR和光学数据）进行连续联合训练（CJT）；2）仅基于新输入的数据（SAR或光学）进行学习而不会忘记（LwF）。对于LwF，集成了均方损失以保持以前学习的功能并防止其仅适合新输入的数据。通过融合基于光学的燃烧区域，考虑到由于周围环境SAR反向散射的可变性而导致的基于SAR的变化提取中的挑战，基于SAR的渐进伪掩模得到了显着改善，这对数据采样和模型训练均有利。预训练的ResNet被冻结为U-Net模型的编码器，训练解码器部分，以逐级方式进一步细化导出的燃烧区域图。实验结果表明，就基于SAR的结果和基于光学的地面真相之间的一致性而言，LwF有可能与CJT相匹配，在悉尼大火（2019–2020）上的F1评分为0.8423，在Chuckegg Creek的F1评分为0.7807火（2019）。我们还观察到，当VH和VV具有不同的时间行为时，SAR交叉极化比（VH / VV）在抑制乘法噪声和检测烧伤区域方面显示出良好的能力。Chuckegg Creek Fire上的7807（2019）。我们还观察到，当VH和VV具有不同的时间行为时，SAR交叉极化比（VH / VV）在抑制乘法噪声和检测烧伤区域方面显示出良好的能力。Chuckegg Creek Fire上的7807（2019）。我们还观察到，当VH和VV具有不同的时间行为时，SAR交叉极化比（VH / VV）在抑制乘法噪声和检测烧伤区域方面显示出良好的能力。