## 2 Materials and Methods
### Methodology
#### 2.3.3 Textural Analysis and Segmentation  
Object-oriented classification generally involves a segmentation stage aiming to group pixels into objects before classification. Then, on an object-by-object basis, a variety of textural, shape, and size information may be computed and used to enhance the classification and avoid the salt-and-pepper effect [56]. In this regard, we combined two steps as performed in previous studies [16; 21]: the Gray-Level Co-occurrence Matrix (GLCM) for the extraction of the textural indices statistics and Simple Non-Iterative Clustering (SNIC) to identify spatial clusters (our image objects). The textural analysis and segmentation step, based on GLCM and SNIC algorithms, was applied only within the OB approaches and on the various dataset combinations used in the study (Table 2). The GLCM algorithm application in GEE computes texture metrics around each pixel of every band. The GLCM is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image. The algorithm requires an 8-bit (gray-level) image as input. As previously undertaken in similar studies [16; 21], this image was generated for S2 and PL data through a linear combination of NIR, red, and green composite bands (Equation 1). For the S1 data, after testing various indices, a normalized difference index was applied to the VV and VH bands (Equation (2)):  
\[(0.3\times\text{NIR})+(0.59\times\text{RED})+(0.11\times\text{GREEN}) \tag{1}\]  
\[(\text{VH}-\text{VV})/(\text{VH}+\text{VV}) \tag{2}\]  
The gray-level images were rescaled in the 0-255 range, using the 2 nd and the 98 th percentiles, before the GLCM application to improve the textural analysis results. After various tests, the window size for GLCM was set at 2 pixels for S2 and S1, and 5 pixels for PL data. Among the many textural indices calculated with GLCM, five main indices were selected for the subsequent steps (Table 4). The layers containing these textural indices, extracted from S2, S1, and PL data, were added to the other layers according to the combinations reported in Table 1 to obtain a multispectral-textural dataset.  
SNIC analysis in this research was performed on the visible and NIR four bands of S2 (10 m) or PL (4.77 m) datasets. After some tests, SWIR of S2 was excluded in this step since it did not improve the image clustering process. The SNIC algorithm is an improved version of the Simple Linear Iterative Clustering (SLIC) algorithm. The algorithm clusters pixels in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels [65]. In GEE, it identifies the objects (clusters) according to the input parameters (compactness, connectivity, neighborhood size, and seed spacing). It generates a multi-band raster including the clusters and additional layers containing the average values of the input features. Although the first three parameters were the same for S2 and PL datasets (1, 8, 128, respectively), the seed spacing was varied to account for the different native spatial resolutions and the expected average object size (S2: 8, PL:10). These values were iteratively selected after many trials and visual analysis of SNIC outputs. The average values of the bands contained in the multispectral-textural datasets were calculated on an object basis using a \"reduce connected component\" step to create the dataset for the subsequent RF classification.