## 4 Results and Discussion
### Results  
We develop experiments using the five classifiers over the following 30 texture feature vectors: (1) Haralick's features: the eight vectors Hf\({}_{\text{df}}\) varying the distance \(d=\{1,2,3,123\}\) and the number of Haralick's coefficients \(f=\{4,5\}\); (2) LBP features: the four vectors LPB\({}_{\text{Ri}}\) where \(R_{i}=\{R_{1},R_{2},R_{3},R_{123}\}\) containing uniform LBP descriptors using eight neighbours and radius of 1, 2, and 3 for \(R_{1}\), \(R_{2}\) and \(R_{3}\) and the concatenation of the previous three vectors; (3) Wavelet features: the eight vectors (DWT\({}_{\text{sfm}}\)) where \(s=\{2,3\}\) means the levels of decomposition, \(f=\{LL,All\}\) means if the decomposition was applied to the low band or over all subbands, respectively, and \(m=\{EEMS,EMS\}\) means if the entropy is considered (EEMS) or not (EMS); (4) Wavelet + cooccurrence features: the six vectors (WCF\({}_{\text{dm}}\)) varying the distance \(d=\{1,2,3\}\) to calculate the cooccurrences matrices and the number of Haralick's coeeticents \(m=\{4,5\}\); and (5) Wavelet + cooccurrence features: the four vectors WDCF\({}_{\text{fm}}\) varying the number of Haralick's coefficients \(m=\{4,5\}\) and the type of decomposition \(f=\{LL,All\}\) using a distance \(d=1\). We developed a total of 150 experiments (30 feature vectors times 5 classifiers).  
Table 2 show the highest kappa achieved by each family of texture features for all the classifiers tested. The best result (kappa = 64.7%) is achieved by the texture vectors WCF\({}_{\text{dm}}\) and the classifier NNET. This set of texture vectors achieved the best performance for classifiers SVM (59.9%) and LDA (50%), while GBM achieved the lowest result (11.8%). In general, the combination of wavelets with second-order statistical characteristics like Haralick's coefficients provided the best results for all the classifiers. The classifier GBM is the best for all the statistical texture features: the local binary pattern (vectors LBP\({}_{\text{Ri}}\)), achieving a kappa = 25.5%, and the textures vectors (HF\({}_{\text{df}}\)), with kappa = 17.8%. The wavelet texture features (vector DWT\({}_{\text{sfm}}\)) achieved the best result (34.3%) for the NNET classifier. But the results provided by the combination of wavelet and cooccurrence features significantly overcame the other texture features for all the classifiers. Therefore, the texture features vectors WCF\({}_{\text{dm}}\) are clearly superior to the others texture descriptors. In relation of the classifier, the NNET achieved the best results for two texture families, while GBM, SVM, and RF are only the pests in one texture feature. The LDA is clearly the worst classifier, as expected, and the other classifiers showed its ability to overcome its baseline performance.  
Table 3 shows the highest kappa achieved by each family of texture features using the best configuration and classifier. The highest kappa (64.7%) was achieved by combining the wavelet decomposition and the Haralick's coefficients energy, correlation, contrast, homogeneity, and entropy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Texture Features** & **LDA** & **GBM** & **NNET** & **RF** & **SVM** \\ \hline Hf\({}_{\text{df}}\) & 3.0 & **17.8** & 17.4 & 13.0 & 13.8 \\ LPB\({}_{\text{Ri}}\) & 15.7 & 25.5 & 6.8 & **34.3** & 10.4 \\ DWT\({}_{\text{sfm}}\) & 3.9 & 6.8 & **34.3** & 11.8 & 26.5 \\ WCF\({}_{\text{dm}}\) & 50.0 & **11.8** & **64.7** & 30.3 & 59.9 \\ WDCF\({}_{\text{fm}}\) & 21.7 & 18.5 & 28.5 & 32.5 & **33.4** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Highest kappa (in %) achieved by each family of texture descriptors for all classifiers tested (in bold letters the best result achieved by each texture vector).  
using a distance \(d=1\) pixels and \(m=5\) coefficients (vector WCF\({}_{15}\)). Table 4 reports the confusion matrix for the best performance in Table 3 (texture vector WCF\({}_{15}\) and classifier NNET), with accuracy of 83%, sensitivity of 88.9% and specificity of 75.0%.