## 2 Materials and Methods
### Methods
#### 2.3.2 Multi-Level Spatial Feature Extraction  
This study also extracted texture, morphological, and deep semantic features from the images to emphasize differences between surface objects for improving classification performance. The gray level co-occurrence matrix (GLCM) algorithm, proposed by [PERSON] et al. [36], is a powerful and adaptable technique, renowned for its effectiveness in texture extraction. The GLCM algorithm captures the relationship between pixel distances and angles, comprehensively reflecting image information through grayscale correlation calculations between two points in the image. Within the GEE platform, nine common texture features were selected to characterize the GLCM algorithm. These features encompass contrast, entropy, correlation, variance, cluster prominence, inverse different moment, dissimilarity, cluster shade, and angular second moment. Subsequently, principal component analysis (PCA) was employed to reduce the dimensions of multiple texture indices obtained from the GLCM algorithm. PCA was conducted within the GEE platform to compute the principal components for the texture feature objects within the SNIC \"clusters\" band. The initial three principal components, covering over 90% of the information, were chosen and incorporated into the feature dataset. Mathematical morphology aims to analyze and process images using mathematical operations. These operations commonly extract, enhance, or modify specific objects or structures in images, such as edges, regions, and contours. Common morphological operations include erosion, dilation, opening, and closing. Erosion and dilation are fundamental operations in morphology. Erosion shrinks the boundaries of objects in an image, eliminating small and insignificant features. Typically, erosion followed by dilation is called opening, and the reverse is closing. Denoting an image as I and a structuring element as SE, the morphological opening and closing of an image with a structuring element are defined as:  
\[\text{ISE}=(\text{I}\Theta\text{SE})\oplus\text{SE}, \tag{1}\]  
\[\text{I}\cdot\text{SE}=(\text{I}\oplus\text{SE})\Theta\text{SE}. \tag{2}\]  
In this study, we conduct PCA dimensionality reduction on the acquired spectral features, resulting in the extraction of the top three principal components. Subsequently, by applying opening and closing operations to four differently shaped structuring ele \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formula** & **References** \\ \hline NDVI & \(\frac{\text{NIR}-\text{RED}}{\text{NIR}-\text{RED}}\) & [30] \\ SAVI & \(\frac{(\text{NIR}+\text{RED}+0.5)\times 1.5}{\text{(NIR}+\text{RED}+0.5)\times 1.5}\) & [31] \\ NDWI & \(\frac{\text{GEEN}-\text{SWIRE}}{\text{GEEN}-\text{SWIRE}}\) & [32] \\ NDTI & \(\frac{\text{NIR}-\text{SWIRE}}{\text{SWIRE}+\text{SWIRE}}\) & [33] \\  & ((RED + SWIR1) - (NIR + BLUE))/((RED + SWIR1) + & [34] \\  & (NIR + BLUE)) & \\ NDBI & (SWIR1 - NIR)/(SWIR + NIR) & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spectral index. ments (line, square, diamond, and disk), we obtain a total of 24 features. After rigorous experimentation, the optimal radius for the structuring elements is found to be 4. After acquiring various image features, we feed the optimized variables into a convolutional neural network (CNN) for deep feature extraction. Specifically, we use a 3 DCNN model built on the PyTorch framework for this task. In Figure 4, Conv3d_1 represents the initial 3D convolutional layer designed to extract primary features from the input data. Padding ensures that the spatial dimensions of the output feature map closely match those of the input to capture boundary information effectively. The MaxPooling layer reduces the spatial dimensions of the feature maps, enhancing features and decreasing computational complexity. Following Conv3d_2, Conv3d_3, and Conv3d_4, continue to extract 96 features. Ultimately, Fc1 transforms the flattened feature map into a 96-dimensional feature set.