## 4 Experiments
### Class Imbalance Experiments  
Almost all land use segmentation datasets have the problem of the predominance of some classes over others. Which classes predominate with respect to the others, this depends on the geographical area, the season, and the extent of the territory analyzed. Furthermore, for this reason, multi-class segmentation for land cover problems through the analysis of satellite images still remains a challenging problem. The number of samples from the training dataset used to estimate the error gradient during training is called the batch size and is an important hyperparameter that affects the resulting trained model. If the dataset is unbalanced, the number of pixels belonging to each class that the neural network uses to compute the gradient depends on the batch size. If the batch is too large then the predominant classes completely crush the classes that have very few samples, while if the batch is small then the number of pixels for each class is more balanced. For this \begin{table}
\begin{tabular}{r r r r r r r r r r r r} \hline \hline  & & \multicolumn{3}{c}{**[20]**} & \multicolumn{3}{c}{**Our (Eval Seb)**} & \multicolumn{3}{c}{**Our (Test Set)**} \\  & **P** & **R** & **F1** & **\#pix.** & **P** & **R** & **F1** & **\#pix.** & **P** & **R** & **F1** & **\#pix.** \\ \hline sugar beet & 91.90 & 78.05 & 84.40 & 59k & 90.95 & 93.66 & 92.29 & 19k & **96.37** & 89.65 & **92.88** & 33k \\ oat & 74.95 & 65.30 & 69.55 & 36k & 79.17 & 68.13 & 73.23 & 23k & **83.28** & **69.88** & **75.99** & 30k \\ meadow & 89.45 & 85.35 & 87.35 & 233k & 89.51 & 87.45 & 88.47 & 149k & **93.27** & **89.53** & **91.36** & 167k \\ rapesed & 95.80 & 92.95 & 94.35 & 125k & 95.98 & 97.64 & 96.81 & 105k & **95.99** & **97.99** & **96.98** & 92k \\ hop & 94.45 & 81.10 & 87.20 & 51k & 95.12 & 93.26 & 94.18 & 71k & **95.55** & **93.94** & **94.74** & 39k \\ spelt & 65.20 & **63.90** & 61.60 & 38k & 66.39 & 51.12 & 57.76 & 17k & **68.02** & 59.69 & 63.58 & 23k \\ trticale & **65.90** & **56.45** & **60.75** & 65k & 57.24 & 36.03 & 44.22 & 34k & 58.20 & 44.59 & 50.49 & 41k \\ beans & 92.60 & 75.15 & 82.40 & 27k & 91.40 & 79.03 & 84.77 & 15k & **95.26** & **88.63** & **91.82** & 19k \\ peas & 77.05 & 56.10 & 64.85 & 9k & **87.07** & 65.26 & 74.61 & 9k & 81.21 & **83.77** & **82.47** & 6k \\ potato & **93.05** & 81.00 & 86.30 & 126k & 91.37 & 91.58 & 91.48 & 74k & 90.94 & **92.32** & **91.63** & 84k \\ soybeans & 86.80 & 79.75 & 82.75 & 21k & 96.05 & **84.33** & **89.81** & 12k & **96.96** & 81.82 & 88.75 & 14k \\ asparagus & 85.40 & 78.15 & 81.60 & 20k & 70.86 & **92.34** & 80.19 & 1k & **95.72** & 84.98 & **90.03** & 14k \\ wheat & 88.90 & 94.05 & 91.40 & 80k & **93.85** & **96.68** & **95.24** & 582k & 92.66 & 95.74 & 94.17 & 531k \\ winter barley & **93.85** & 89.75 & 91.70 & 258k & 93.52 & 95.01 & **94.26** & 214k & 93.19 & **95.28** & 94.23 & 170k \\ rye & 81.15 & 54.45 & 64.60 & 43k & **83.58** & 57.51 & 68.14 & 15k & 82.36 & **59.14** & **68.85** & 25k \\ summer barley & 82.70 & 85.95 & 84.15 & 73k & **84.94** & 87.71 & 86.30 & 52k & 84.60 & **89.54** & **87.00** & 52k \\ maize & 91.95 & 96.55 & 94.20 & 919k & 96.74 & **98.03** & **97.38** & 713k & 96.44 & **98.19** & 97.31 & 604k \\ \hline weighted avg. & 89.70 & 89.60 & 89.40 & & **93.22** & **93.55** & **93.39** & & 92.94 & 92.68 & 92.81 & \\ \hline  & & Overall Accuracy & 89.60 & & Overall Accuracy & **93.55** & & Overall Accuracy & 92.94 \\  & & Overall Kappa & 0.87 & & Overall Kappa & **0.92** & & Overall Kappa & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical results of the comparison made with what was published in paper of [PERSON] and [PERSON]. Our model uses a Resnet101, trained for 300 epochs on the training set. We report the comparison measures on the test set and on the validation set (we did not do hyperparameter tuning). In this experiment no techniques or weights were used to solve the problem of unbalanced classes. All measurements, except the Kappa, are in percentages.