## 4 Discussion
### Objective Assessment
#### 4.2.2 Assessment of Test Image B  
For test image B in Figure 6a, the related resulting accuracy data are summarized in Table 2. From Table 2, higher values of OA are achieved in the resulting images produced by the SDI-based method [19] (93.27%) and our proposed approach (94.24%) than those by other methods [11; 14; 18; 20], as shown in the last column of Table 2. These higher OA values mean that the SDI-based method and our method performed better generally at detecting shadow pixels than the other methods. Furthermore, for the PA values and the EO values, quite high PA values and corresponding low EO values were obtained in resulting images produced by the SRI-based method [11] (PA: 97.16%, EO: 2.84%), the NSVDI-based method [18] (PA: 99.94%, EO: 0.06%), the SDI-based method [19] (PA: 94.99%, EO: 5.01%) and our method (PA: 95.63%, EO: 4.37%). The high PA and corresponding low EO metrics of methods in [PERSON] [11], [PERSON] et al. [18], and [PERSON] et al. [19] and our method together reveal that shadow pixels of the original images were correctly identified, and fewer shadow pixels of the original images were omitted as nonshadow pixels for test image B in Figure 6a, when compared with other shadow detection methods in [PERSON] et al. [14], and [PERSON] et al. [20]. Additionally, for the SP values and the EC values, higher SP values and corresponding lower EC values were obtained by the SI-based method [14] (SP: 93.70%, EC: 6.30%), the SDI-based method [19] (SP: 90.21%, EC: 9.79%)and our method (SP: 91.76%, EC: 8.24%) than those by other methods in [PERSON] [11], [PERSON] et al. [18], and [PERSON] et al. [20]. These higher SP and corresponding lower EC values together show that these two standard shadow detection methods and our method are more correct when classifying nonshadow pixels in the original images to the same class rather than as shadow pixels, and lower probability of misclassifying nonshadow pixels in the original images as shadow pixels than those by other methods in [PERSON] [11], [PERSON] et al. [18], and [PERSON] et al. [20]. Besides, even though the PA value of our method (95.63%) was a little lower than that of the SRI-based method in [PERSON] [11] (97.16%) and the NSVDI-based method in [PERSON] et al. [18] (99.94%), the SP (91.76%) and OA (94.24%) values of our method were much higher than those of the above two methods. Similarly, even though the EO value of our method (4.37%) was higher than that of the SRI-based method in [PERSON] [11] (2.84%) and the NSVDI-based method in [PERSON] et al. [18] (0.06%), the EC (8.24%) value of our proposal was much lower than those of the above two methods. Specifically, our method also performed better for shadow detection with a somewhat higher PA (95.63%), SP (91.76%), and OA (94.24%), as well as lower EO (4.37%), and EC (8.24%), than the SDI-based method in [PERSON] et al. [19] with PA (94.99%), SP (90.21%), and OA (93.27%), as well as EO (5.01%) and EC (9.79%). Both the significant metrics of high PA and SP values, as well as low EO and EC values reveal that our method can simultaneously perform well in reducing the probability of omitting shadow pixels in the original images as nonsmadows, and misclassifying nonshadow pixels in the original images as shadows for test image B, as shown in Figure 6a.