## 3 Results  
### Segmentation Quality and Classification Accuracy  
Table 3 reports the overall segmentation quality and the classification accuracy. The segmentation quality is evaluated using the number-of-segmentation ratio (NSR), potential segmentation error (PSE), and Euclidian Distance 2 (ED2) for the reference image scene (HOPE) and the candidate image scenes. With respect to the NSR metric, segmentation results from HOPE exhibited the highest arithmetic correspondence while ZAVO exhibited the lowest arithmetic correspondence between the reference objects and the image object candidates. Image segments from PAUL, SPIN, and BIRD reported relatively high NRS values. When analyzing PSE values of each site, BIRD and ZAVO reported the highest geometrical congruency between the manually-digitized targets and the image object candidates. In contrast, despite the high arithmetic fit, HOPE reports the worst geometrical agreement. According to the ED2 metric, which gauges the arithmetic- and geometrical-fit simultaneously, HOPE and ZAVO exhibit the lowest and the highest values, respectively, indicating best and worst segmentation quality. Of the other sites, AITC and BAIL showed relatively low ED2 values manifesting mediocre segmentation quality. The classification accuracy is reported for both the master ruleset and the adapted ruleset based on three parameters: Completeness, Correctness, and Quality. It should be noted that for each of the test-bed sites (AITC, BAIL, BIRD, PAUL, SPIN, and ZAVO), the classification accuracy was individually assessed based on the results obtained by applying the reference ruleset and the adapted ruleset. When the reference ruleset is executed without adaptations, in terms of Correctness, SPIN and ZAVO reported the highest (62.1%) and the lowest (0.0%) detection of targets, respectively. However, compared to HOPE, SPIN exhibited inferior results. In the case of the Completeness metric, BIRD and ZAVO showed the best (51.4%) and worst (0.0%) results while PAUL and AITC marked relatively poor metric values. When comparing metric scores of BIRD and HOPE, the latter shows inferior classification accuracy with respect to the Completeness metric. Even though SPIN reported the highest Correctness, it showed relatively low completeness (20.3%). When analyzing scores reported for the Quality (combined metric of correctness and completeness) metric when the reference ruleset is executed without adaptations, BAIL had the highest classification accuracy (36.8%) while ZAVO reported the lowest accuracy (0.0%). Of the others, BIRD exhibited relatively high classification accuracy (34.3%) while PAUL marked notably low accuracy (9.5%). Similar to Correctness and Completeness, HOPE reported superior results for the Quality metric (55.9%) compared to that of all the testbeds. This highlights the site- and scene-dependency of the master ruleset.  
When considering classification accuracy with respect to the adapted ruleset, BAIL and AITC had the highest (79.1%) and the lowest (29.3%) correctness, respectively. ZAVO also yielded a high value (73.5%) and BIRD, PAUL, and SPIN marked comparable scores. Of the testbeds, BAIL and ZAVO reported superior results to HOPE (70.0%). Generally speaking, we can deduce that the adaptations of fuzzy rules have improved the correctness of classification. This fact is well highlighted in ZAVO, where the Correctness metric has been improved from 0.0% to 73.5%. In the case of Completeness, BIRD reported the highest score, which exceeds HOPE while AITC reported the lowest value. Of the other testbeds, BAIL, PAUL, SPIN, and ZAVO yielded high metric scores and the former three exceeded HOPE. In terms of overall classification quality, BAIL was classified with the highest accuracy (66.4%) while AITC had the worst accuracy (23.1%). Overall, BAIL, BIRD, PAUL, SPIN, and ZAVO outperform the accuracy of HOPE, which indicates the success of adaptation.