# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Classification Results  
The classification of the S-1 image (6 April 2017) at the date of the flood event was performed based on a S-1 time series layer stack for the period between 2 September 2016 and 23 July 2017. Validation of the classification was performed using the S-2-based reference flood mask. Figure 9a shows the pixel-based classification result of the time series approach for the investigated area in Namibia, which includes the classes permanent open water, TOW, TFV, and Dry Land, while Figure 9b represents the object-based classification result. For visual comparison, the validation mask is shown in Figure 2b. A visual comparison of the two classification images with the validation mask reveals the clear similarity of the area extent for each class. As expected, the pixel-based classification appears to be slightly noisier in comparison to the object-based classification. Figure 8: Importance of the time series features, which were determined by the Random Forest algorithm for TOW (blue bars) and TFV (green bars). The features are sorted by their highest to lowest importance. Figure 10 shows the intersection between the validation data and the results based on pixel-based (a) and object-based (b) classification, respectively. Areas of correspondence, as well as misclassified areas, can be shown by this intersection. The orange areas, which are marked as TFV in the classification, however, representing Dry Land in the validation data, can be visually identified as largest misclassification areas. Another significant misclassification can be identified by yellow areas. These areas represent the Dry Land class in the classification image and the TFV class in the validation data, and are slightly larger in the pixel-based classification compared to the object-based classification. Recognisable are also bright blue areas, which can be found at the transect zone between Dry Land and TOW. While these areas represent Dry Land in the reference data, they are classified as TOW. The visual comparison between pixel and object-based classification shows that the extent of bright blue areas in both classifications is similar; this is also the case for all other remaining small misclassified areas. A further difference between pixel- and object-based classification can be observed in the red areas, which represent Dry Land in the classification and TOW in the validation data. Overall, the comparison between pixel- and object-based classification shows that fewer misclassified areas occur in the object-based classification. Figure 10: Areas of correspondence and misclassifications for the classes TOW, TFV, and Dry Land using pixel-based (**a**) and object-based (**b**) classification, which are generated by the intersection of the classifications and the validation data. Figure 9: Pixel-based classification result (**a**) and object-based classification result (**b**) for the study area in Namibia. The quantification of the classification accuracy of TOW, TFV, and Dry Land was performed using OA, producer accuracy (PA), user accuracy (UA), and Kappa index (K). The accuracies for pixel-based and object-based classification are shown in Figure 11. The accuracy values (UA and PA) for the TOW class are 85.1% and 82.6% for pixel-based classification, and 85.8% (UA) and 85.5% (PA) for object-based classification, respectively. In comparison, the accuracy of the class TFV is lower, with 67.9% (UA) and 86.32% (PA) for pixel-based classification, and 76.1% (UA) and 91.2% (PA) for object-based classification. For the class Dry Land, the UA and PA are 74.8% and 59.0% for the pixel-based classification. For the object-based classification, these are 80.8% (UA) and 67.0% (PA), respectively. The improvement was mainly achieved in the UA area for TFV by 8.1% and PA for Dry Land by 8.0%. Overall, the OA of object-based classification is about 5.0% higher compared to pixel-based classification. The Kappa index is also 0.08 higher for the object-based classification.