## 2 Materials and Methods
### Proposed Method  
Segmentation and classification of flooded areas using 3D CNN for the SAR image dataset and the flood factor consists of three-dimensional dataset segmentation stages using three-dimensional CNN to get initial segmentation results. These results are used to weight neuron connections to perform \(n\)-dimensional optimization so that we get the classification of pixels into flood or other categories.  
In the three-dimensional CNN shown in Figure 8, several CLs with dimensions a \(\times\) a \(\times\) a are used to filter the input data to obtain a feature map. The input data used are shown in Table 1. The images are down sampled using a pooling layer by summarizing the features present in the images. In this model, the pooling layer uses max pooling, which summarizes the most dominant value in the sample. To prevent edge and corner pixels from being omitted by the model, valid padding is used on the input layer and the CL. The padding basically left the image unchanged but allows edge and corner pixels to be more sampled as it is now placed further from the edge. Furthermore, the pooling layer measuring b \(\times\) b \(\times\) b is used to reduce the map, so those neuron connections are formed to compile the information obtained, which is then formed into FCL. FCL stored the different feature values and compiled them into a feature map with two output categories, namely flood pixels and non-flood pixels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Source** & **Resolution/Scale** & **Acquisition Date** \\ \hline Co-polarization (VV) SAR data & Sentinel-1a & 10 m & 21 November 2019 to 20 October 2020 \\ Cross-polarization (VH) SAR data & Sentinel-1a & 10 m & 21 November 2019 to 20 October 2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data used for the 3D-CNN.  
Figure 8: Representation of 3D-CNN process.  
Figure 7: Representation of the multi-temporal 2D dataset into 3D data.  
The stages carried out in this study began with an inventory of the data used for classification, namely the SAR image dataset. The pre-processing stage is comprised of registering the image data to ensure that the coordinates are consistent between different images. As the images are in RGB IIF format with r \(\times\) c \(\times\) 3 dimensions, they must be converted first into grayscale images, and then samples of sub-images were selected that represent flood and non-flood targets. The data are then divided into training and validation sets. The Feature Learning stage, or training, provides training data for the model to store known flood data. The commonly used proportion between the two sets is 70:30 [54], but we also include 80:20 and 90:10 for comparisons. Training data are used to train 3D CNN [36] in determining the parameters' optimized values. The next stage is to conduct training on the classification by three-dimensional CNN to detect the presence of water surfaces and differentiate them from other surfaces by the variance of the pixel values since dry land and permanent water bodies have consistent values. The ReLU plays a significant part in this phase since flood areas tend to change values, the possibility of dry land changes to a water surface and then back to dry land will result in a negative value. The ReLU rectifies this problem and prevents the neuron with a negative output from being contributed to the network. The Classification stage presents the system to other data for recognizing if there are flood features present in the images using feedback from the results of the Training stage. The overall process in the research is shown in Figure 9.