Abstract
The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the static characteristics and ignores the temporal evolution in the life cycle of MCS. In this paper, we propose a novel encoder-decoder neural network for MCS detection(MCSDNet). MCSDNet has a simple architecture and is easy to expand. Different from the previous models, MCSDNet targets on multi-frames detection and leverages multi-scale spatiotemporal information for the detection of MCS regions in remote sensing imagery(RSI). As far as we know, it is the first work to utilize multi-scale spatiotemporal information to detect MCS regions. Firstly, we design a multi-scale spatiotemporal information module to extract multi-level semantic from different encoder levels, which makes our models can extract more detail spatiotemporal features. Secondly, a Spatiotemporal Mix Unit(STMU) is introduced to MCSDNet to capture both intra-frame features and inter-frame correlations, which is a scalable module and can be replaced by other spatiotemporal module, e.g., CNN, RNN, Transformer and our proposed Dual Spatiotemporal Attention(DSTA). This means that the future works about spatiotemporal modules can be easily integrated to our model. Finally, we present MCSRSI, the first publicly available dataset for multi-frames MCS detection based on visible channel images from the FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieve the best performance on MCS detection task when comparing to other baseline methods.
Abstract (translated)
准确检测 Mesoscale Convective Systems (MCS) 对气象监测至关重要，因为它们有可能通过严重的天气现象（如冰雹、雷暴和重雨）造成重大破坏。然而，现有的 MCS 检测方法主要针对单帧检测，这仅仅考虑了静态特征并忽略了 MCS 生命周期的时间演化。在本文中，我们提出了一个新颖的编码器-解码器神经网络用于 MCS 检测（MCSDNet）。MCSDNet 具有简单的架构，易于扩展。与之前模型不同，MCSDNet 针对多帧检测，并利用遥感和气象卫星图像中的多尺度时空信息来检测 MCS 区域。据我们所知，这是第一个利用多尺度时空信息检测 MCS 区域的 work。首先，我们设计了一个多尺度时空信息模块，以提取不同编码器级别下的多层语义，这使得我们的模型可以提取更详细的时空特征。其次，引入了一个 Spatiotemporal Mix Unit（STMU），它可以捕捉帧内特征和帧间关联，是一个可扩展的模块，可以替代其他时空模块，例如 CNN、RNN、Transformer 和我们提出的双时空注意（DSTA）。这意味着未来关于时空模块的工作可以轻松地集成到我们的模型中。最后，我们提出了 MCSRSI，第一个基于 FY-4A 卫星可见通道图像的多帧 MCS 检测公开数据集。我们还对 MCSRSI 进行了多项实验，并发现我们提出的 MCSDNet 在 MCS 检测任务上与其他基线方法相比实现了最佳性能。
URL
https://arxiv.org/abs/2404.17186
