## 5 Results and Discussion  
### Classification Accuracy with Regard to Data Sources and Features  
Three main datasets were tested in this work (_i.e._, only Landsat 8 time series data, only WV2 single orthoimage data and, finally, Landsat 8 time series plus WV2 single orthoimage data). In addition, a special case involving only the single Landsat 8 orthoimage taken on 12 September was conducted just to be compared with the single WV2 case taken also in September. Moreover, four strategies or groups of features were considered for each dataset: (1) all features (All); (2) all features without GLCMs (no GLCM); (3) all features without GLCMs, mean values (no GLCM, mean); and (4) all features without GLCMs, mean and standard deviation values (no GLCM, mean, SD). Figure 4 shows the accuracy per class and OA based on the 10-fold cross-validation method for the different datasets tested. It can be noted that the best OA values were attained with the two first strategies. Furthermore, the second-order textural parameters based on GLCM added no information in order to improve the classification accuracy. Working on outdoor crops identification, [PERSON] _et al._[51] reported that texture features achieved extremely poor overall accuracy as compared to spectral features. However other research found that texture attributes based on GLCM were relevant to discriminate between some crops with similar spectral responses [24, 25]. In this  
sense, it is important to bear in mind that the calculation of GLCM is computationally intensive and very time consuming. Notice that, apart from GLCM descriptors, textural information is somehow included by means of SD values, which can be considered as first-order textural features. The best OA value (81.3%) was reached when using no GLCM strategy on Landsat 8 time series (Figure 4a). When the Landsat 8 + WV2 dataset was employed, a slightly lower OA value of 79.4% was achieved with the no GLCM strategy (Figure 4b). OA values were far worse when only a single growing season period was considered, highlighting the suitability of multi-temporal images taken along the growing season to improve under greenhouse crop discrimination. In this way, and applying the no GLCM strategy, OA values of 75.1% and 67% were achieved for WV2 and Landsat 8 September datasets, respectively (Figure 4c,d). The better classification results found in the case of WV2 can be attributed to its much higher spatial resolution. Regarding the results from the third strategy (no GLCM, Mean) depicted in Figure 4, it should be noted that object spectral information based on mean values could not be removed without causing a decrease in OA close to 2%. This result strengthens the importance of applying proper atmospheric corrections on the original images. Figure 4: Per-class accuracy given by the corresponding \(F_{\beta}\) index and overall accuracy (OA) using different groups of features and data sources. (**a**) Landsat 8 time series (eight orthoimages); (**b**) Landsat 8 time series plus WV2 single orthoimage; (**c**) WV2 single orthoimage taken on September 30; (**d**) Image Landsat 8 taken on September 12. The OA values of around 80% achieved in this work are rather promising as compared to those reported by other authors working with a similar methodology, but on outdoor crops. For instance, [PERSON] _et al_. [24] reached an OA of 79% applying the DT classifier for detecting 13 major crops cultivated in the agricultural area of Yolo County, California, USA. This classification accuracy rose up to 88% by using support vector machine (SVM) or multilayer perceptron (MLP) neural network classifiers [51]. In another study, twelve outdoor crops and two non-vegetative land use classes were classified with 80.7% OA at cadastral parcel levels through multi-temporal remote sensing images [48]. Furthermore, working on mapping outdoor crops (classes corn, soybean and others), an OA of 88% was reported by [PERSON] _et al_. [2]. Finally, [PERSON] _et al_.