## 4 Methodology
### Experiment  
In the first experiment, spectral indices (NDVI, DVI, and SR) were used for layer stacking in two combinations of Sentinel-2 bands. Five-band layer stacking included B4, B3, B2, B8, and NDVI. Seven-band layer stacking was obtained by adding two more indices: DVI and SR. All bands have a normalized interval with zero-center normalization in the image input layer of the algorithms. Zero-center normalization typically means that images are normalized with a mean of 0 and a standard deviation of 1. In the Sentinel dataset, the training data consisted of three images of 1311 \(\times\) 1287 pixels. The validation data consisted of an image of 1311 \(\times\) 1287 pixels. Two testing data images were each 689 \(\times\) 1101 pixels. The Sentinel dataset contained approximately 17% validation, 15% testing, and 68% training data. In CNN-based deep learning models according to the different patch sizes, proper batch sizes were set. CNN sample patch window refers to patch size, and it is an example of a convolution input sample. In this experiment, 128 \(\times\) 128 patch size was determined as 16 batch size, 64 \(\times\) 64 patch size as 32 batch size, and 32 \(\times\) 32 patch size as 128 batch size. Batch size refers to how many data will be processed simultaneously in the DL process. The most suitable batch size parameters were selected according to the hardware features used. Moreover, stochastic gradient descent (SGD) was used as an optimization algorithm. The learning rate is a key parameter for the SGD algorithm [26]. In the experiment, the initial learning rate of 0.01 was applied. For each experiment, different and proper epoch numbers were set for the deep learning-based models. In the second experiment, on the Zurich dataset, the same workflow was conducted. The dataset was split into testing, training, and validation parts. The obtained results were compared with state-of-the-art models in the literature. The change in the image particle size was also a variable in the learning model, affecting the optimization in selecting other hyperparameters. Thus, optimum epoch numbers were determined experimentally. After hyperparameter determination, two deep learning methods were trained with selected hyperparameters using three different patch sizes (32 \(\times\) 32, 64 \(\times\) 64, and 128 \(\times\) 128). All pixels in the images had labeled class categories according to deep learning-based learning models. Afterward, the classification results \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} \\ \hline  & \multicolumn{2}{c}{**Correct**} & \multicolumn{2}{c}{**Incorrect**} \\ \hline Model 1 & Correct & \(f_{11}\) & \(f_{12}\) \\ Model 2 & Incorrect & \(f_{21}\) & \(f_{22}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contingency matrix between two models. of the CNN models were post-processed by applying a median filter and MRS by using the semantic segmentation images. Median filtering is a nonlinear function often used for diminishing noise [52]. The function replaces pixels with the median of neighboring pixels by using a kernel-size window. The literature contains several applications of spatial filter types for LULC tasks [53; 54], including to reduce speckle effect and preserve edges. The kernel size of the median filter was selected as 5 \(\times\) 5 for the Sentinel dataset and 3 \(\times\) 3 for the Zurich Summer dataset. In the study, the experiments were carried out with a computer with i7 7700 HQ 2.8 GHZ, 16 GB RAM, and GTX1050 Ti 4 GB graphics card. The experiments were implemented in MATLAB software. The algorithm of MRS is preferred in the CNN-MRS model to protect feature information of pixels. MRS is the first step of object-based image classification (OBIA) applications. In experiments, MRS was performed using eCognition Developer v.9.3, and the images were divided into segments including a seven-band stack image.