## 5 Performance Evaluation of Classification Algorithms
### What are the Most Important Image Features for Class Separability?
A description of the features can be found in Tables 2 and 3.  
features (e.g., mean and standard deviation values per image band, NDVI, _etc._) account for 51%, textural features (e.g., GLCM entropy, GLCM contrast, _etc._) for 35% and geometrical features (e.g., contour convexity, perimeter, _etc._) for 14% of the total score on the TM dataset. Amongst the ten most important features for the TM, spectral and textural features are equally well represented, while geometrical features are not represented. For MSS, the average quality score per feature class splits into 67% spectral, 22% textural and 11% geometrical. Moreover, eight out of the ten most important features are spectral, while the most important image bands seem to be band 3 (0.7-0.8 \(\upmu\)m) and 4 (0.8-1.1 \(\upmu\)m). The ranking of the features derived for the QB reference dataset shows a distribution of the average quality score into 60% spectral, 28% textural and 12% geometrical. Looking at the ten most important features of QB, all four image bands show high importance for built-up area recognition, both in the spectral and the textural domain. Important features that appear for all the image types are NDVI and the mean spectral values of band 4 (Near Infra-Red (NIR)) calculated over the segments. \begin{table}
\begin{tabular}{c c c c} \hline _Feature ID_ & _Image Feature Description_ & _Feature Class_ & _Implementation_ \\ \hline
**mean\_bx** & Mean spectral value in image band \(x\) & spectral & OpenCV \\ std\_bx & Standard deviation in image band \(x\) & spectral & OpenCV \\  & Mean value of NDVI; & & \\
**ndvi\_mean** & \(NDVI=\big{(}NIR-\text{Red}\big{)}\big{/}\big{(}NIR+\text{Red}\big{)}\) & & \\
**ndvi\_std** & Standard deviation of NDVI & spectral & custom C/C++ \\
**max\_pixel\_bx** & Maximum brightness value in image band \(x\) & spectral & custom C/C++ \\
**min\_pixel\_bx** & Minimum brightness value in image band \(x\) & spectral & custom C/C++ \\  & Weighted brightness, with \(J\) being the number of & & \\  & image bands, \(J\) being the number of pixels per segment & & \\
**wB** & and \(p\) being the brightness values of the pixels; & spectral & custom C/C++ \\  & & \\  & Angular Second Moment derived from the GLCM* in & & \\
**asm\_bx** & band x; & \(ASM=\sum_{i,j=0}^{N-1}P_{i,j}\) & textural & custom C/C++ \\  & Contrast derived from the GLCM* in band \(x\); & & \\
**contrast\_bx** & \(CONT=\sum_{i,j=0}^{N-1}P_{i,j}\big{(}i-j\big{)}^{2}\) & textural & custom C/C++ \\  & Dissimilarity derived from the GLCM* in band \(x\); & & \\
**dissimilarity\_bx** & \(DIS\)\(=\sum_{i,j=0}^{N-1}P_{i,j}\big{|}i-j\big{|}\) & textural & custom C/C++ \\  & Entropy derived from the GLCM* in band \(x\); & & \\
**entropy\_bx** & \(ENT\)\(=\sum_{i,j=0}^{N-1}P_{i,j}\big{(}-\text{ln}P_{i,j}\big{)}\) & textural & custom C/C++ \\  & Correlation derived from the GLCM* in band \(x\); & \\
**glcm\_correlation\_bx** & \(CORR=\sum_{i,j=0}^{N-1}P_{i,j}\left[\frac{\big{(}i-\mu_{i}\big{)}\big{(}j-\mu_{ j}\big{)}}{\sqrt{\big{(}\sigma_{i}\big{)}\big{(}\sigma_{j}\big{)}}}\right]\) & textural & custom C/C++ \\ \hline \end{tabular}
\end{table} 
Table 2: List of image features (part I of II). All image features are computed per segment. **Table 2.**_Cont._  
\begin{tabular}{c c c c} \hline _Feature ID_ & _Image Feature Description_ & _Feature Class_ & _Implementation_ \\ \hline \multicolumn{4}{c}{Mean derived from the GLCM* in band \(x\);} \\ \multicolumn{4}{c}{**glcm_meani_bx**} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c