## 4 Discussion
### Objective Assessment
#### 4.2.3 Assessment of Test Image C  
For test image C in Figure 7a, the related accuracy data are listed in Table 3. From Table 3, higher OA values are noted in the resulting images produced by the SDI-based method in [PERSON] et al. [19] (89.26%) and our proposed approach (95.50%) than those by the SRI-based method in [PERSON] [11] (75.60%), the NSVDI-based method in [PERSON] et al. [18] (74.89%), the MC3-based method in [PERSON] et al. [20] (81.75%) and the SI-based method in [PERSON] et al. [14] (75.06%), as shown in the last column of Table 3. These higher OA values reveal that both the SDI-based method in [PERSON] et al. [19] and our approach were generally more effective at detecting shadows for test image C than other methods in [PERSON] [11], [PERSON] et al. [14], [PERSON] et al. [18], and [PERSON] et al. [20]. Furthermore, in terms of the PA values and the corresponding EO values, higher PA and corresponding lower EO values were obtained by the SRI-based method in [PERSON] [11] (PA: 98.88%, EO: 1.12%), the NSVDI-based method in [PERSON] et al. [18] (PA: 99.72%, EO: 0.28%) and our approach (PA: 97.19%, EO: 2.81%) than those by the three other methods in [PERSON] et al. [14], [PERSON] et al. [19], and [PERSON] et al. [20], as shown in the second and the fourth columns of Table 3. These high PA and corresponding low EO values show the fact that shadow pixels in the original images are correctly identified, and the omission of shadow pixels in the original images is mostly avoided with these three methods (i.e., the SRI-based method in [PERSON] [11], the NSVDI-based method in [PERSON] et al. [18] and our approach), when compared with those of the other shadow detection methods in [PERSON] et al. [14], [PERSON] et al. [19], and [PERSON] et al. [20]. Additionally, regarding the SP values and the corresponding EC values, rather high SP and low EC values were achieved by the SI-based method [14] (SP: 96.64%, EC: 3.06%), the SDI-based method [19] (SP: 94.45%, EC: 5.55%) and our method (SP: 92.10%, EC: 7.90%) in comparison with those by the other methods, the SRI-based method in [PERSON] [11] (SP: 28.69%, EC: 71.31%), the NSVDI-based method in [PERSON] et al. [18] (SP: 24.87%, EC: 75.13%) and the MC3-based method in [PERSON] et al. [20] (SP: 76.17%, EC: 23.83%). For shadow detection methods in [PERSON] et al. [14], [PERSON] et al. [19] and our approach, the SP and corresponding EC values above reveal that nonshadow pixels in the original images were better discriminated from shadow pixels, and fewer nonshadow pixels in the original images were misclassified as shadow pixels than the other shadow detection methods in [PERSON] [11], [PERSON] et al. [18] and [PERSON] et al. [20] for test image C in Figure 7a. Besides, the problems of shadow omission or nonshadow misclassification could be effectively resolved solely with standard shadow detection methods in [PERSON] [11], [PERSON] et al. [14], [PERSON] et al. [18], and [PERSON] et al. [19], because rather high PA and low SP values, as well as low EO and high EC values, were obtained by the SRI-based method in [PERSON] [11] (PA: 98.88%, SP: 28.69%, EO: 1.12%, EC: 71.31%) and the NSVDI-based method in [PERSON] et al. [18] (PA: 99.72%, SP: 24.87%, EO: 0.28%, EC: 75.13%), and quite high SP and low PA values, as well as low EC and high EO values, were obtained with the SI-based method in [PERSON] et al. [14] (PA: 64.19%, SP: 96.64%, EO: 35.81%, EC: 3.06%) and the SDI-based method in [PERSON] et al. [19] (PA: 86.68%, SP: 94.45%, EO: 13.32%, EC: 5.55%). However, our shadow detection approach was more effective at simultaneously resolving both problems of shadow omission and nonshadow misclassification than other shadow detection methods investigated in this paper, because high PA (97.19%) and SP (92.10%) values as well as low EO (2.81%) and EC (7.90%) values were achieved with our method rather than either one or none in other methods for test image C in Figure 7a.