# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 1 Introduction
Therefore, joint, one-stage approaches, where both segmentation and change detection are learned together, are increasingly favored. An example of this is the work by [25], in which a model with a Siamese architecture is used to process the images of different time-steps and to extract a common feature representation to perform direct CD and prevent error propagation. However, CNNs encounter difficulties when applied to extensive flooding areas since an increased receptive field results in high computational complexity. Moreover, they are only able to model short-range dependencies, making them unable to capture essential global semantic context information that is required for a practical semantic segmentation task [26]. To alleviate these challenges, a spatial and channel attention mechanism has been introduced to CNNs to allocate computational resources to more relevant flood features. [PERSON] [27] proposed WaterDetectionNet that incorporated self-attention and multiscale feature learning into an encoder-decoder architecture with the Xception backbone of DeepLabv3+, to facilitate a better flood-mapping result. Similarly, [PERSON] [28] integrated spatial and channel attention mechanisms with the Inception v1 network and showed enhanced results for Sentinel-1 flood images under a low data regime. These attention-augmented CNNs provide a potential direction of development with the penalty of computation effort. Besides flood detection, CNN architectures such as CENet [29] and dilated fully convolutional networks with active contour models [30] pushed the segmentation and boundary refinement in medical imaging. Their developments on context extraction and edge profiling have inspired similar progress on flood segmentation, suggesting the viability of introducing auxiliary modules with the intention of dealing with the intrinsic limitation of the CNN. The U-Net is a well-known flood detection model because of its high precision on pixel-level segmentation. With the pixel-level segmentation accuracy, U-Net has become a dominant flood detection model. Its encoder-decoder structure is symmetric with skip connections to preserve spatial resolution and fine detail, which may otherwise be lost in typical CNN pooling layers [31]. Such a feature-reuse mechanism enables U-Net to better identify flood boundaries, which are critical to flood extent mapping. The state-of-the-art advancements of the original U-Net have improved boundary precision and multiscale information. For instance, [PERSON] [32] proposed BASNet, which employs residual refinement modules and hybrid loss functions to enhance regional and boundary segmentation quality at the same time. Similarly, [PERSON] [33] applied this method for the fusion of Sentinel-1 SAR with Sentinel-2 multispectral data, showing competitive mIoU scores greater than 50%, indicating the strength of cross-modal feature extraction. Moreover, U-Net++ [34] further enhances the U-Net model using nested dense skip connections, which reduce the semantic gap between encoder and decoder features, leading to smoother transitions of features and increasing the segmentation accuracy [35]. This recursive approach is particularly beneficial in the case of flood, where water accounts and shape can be found. Despite these achievements, U-Net-based architecture struggles in the presence of speckle noise and cluttered backgrounds often present in SAR images. [PERSON] [36] proposed SA-U-Net, which contains spatial attention modules and structured dropout convolutional blocks for learning discriminating features as well as addressing overfitting on data-scarce cases. Similarly, [PERSON] [37] introduced multihead attention into the U-Net architecture designed for SAR image characteristics that led to an incremental improvement of above 3% accuracy, precision compared to the baseline of U-Net in delineating the flood changes.