## 4 Discussion
### Objective Assessment
All of these metrics described above (PA, EO, SP, EC, and OA) were employed together to assess the performance of the comparable standard shadow detection algorithms and our proposed method for detecting shadow pixels and correctly differentiating shadow pixels from vegetation. These metrics above were used to objectively demonstrate the accuracy of each shadow detection method previously reported [11; 14; 18; 19; 20] and our proposed method in terms of shadow detection accuracy, which was based on the calculation between each resulting image and the corresponding reference image at the pixel level. The achieved accuracy data are summarized in Table 1 for test image A, shown in Figure 5a, Table 2 for test image B, shown in Figure 6a, and Table 3 for test image C, shown in Figure 7a. For objectively assessing the effectiveness of our proposed shadow  
4.22%). However, rather low PA values and corresponding high EO values were obtained by the SRI-based method [11] (PA: 75.40%, EO: 24.60%), the MC3-based method [20] (PA: 86.28%, EO: 13.72%), and the SI-based method [14] (PA: 62.64%, EO: 37.36%). The PA and EO values above reveal that the NSVDI-based method [18], the SDI-based method [19] and our proposed MPSI-based approach performed better in correctly classifying shadow pixels of original shadow to the same class rather than nonshadow pixels in the produced images. They avoided omitting shadow pixels as nonshadow pixels for test image A in Figure 5a when they were compared with the three other methods above [11; 14; 20]. As for metrics of the SP values and the EC values, we observed that high SP values and corresponding low EC values were obtained by the SRI-based method [11] (SP: 92.98%, EC: 7.02%), the SI-based method [14] (SP: 97.03%, EC: 2.97%), and our proposed approach (SP: 94.77%, EC: 5.23%); whereas, relatively low SP values and high EC values were shown by the NSVDI-based method [18] (SP: 81.52%, EC: 18.48%), the MC3-based method [20] (SP: 73.06%, EC: 26.94%), and the SDI-based method [19] (SP: 86.16%, EC: 13.84%). The SP and EC values above show that the former three methods--the SRI-based method [11], the SI-based method [14], and our proposed approach--performed better in correctly classifying nonshadow pixels of the original images to the same class rather than as shadow pixels, and avoided wrongly identifying nonshadow pixels as shadow pixels for test image A, as shown in Figure 5a, than the three other methods. In general, even though the resulting images produced by the SRI-based method [11] and the SI-based method [14] had high SP values and corresponding low EC values, they had rather low OA values, low PA values, and corresponding high EO values. Besides, even not very high SP values and very low EC values for the two methods [18; 19], they had significantly high PA values and low EO values, and high OA values. All of the above reveals the better overall performance in terms of shadow detection by the methods in [PERSON] et al. [18] and [PERSON] et al. [19] than those in [PERSON] [11], [PERSON] et al. [14], and [PERSON] et al. [20]. When compared with the accuracy data of these shadow detection methods for test image A, as shown in Figure 5a, the PA value for our proposal (95.78%) is a little lower than that (96.18%) of the SDI-based method [19], and the SP value of our proposal (94.77%) is lower than that (97.03%) of the SI-based method [14]. Similarly, the EO value of our proposal (4.22%) is a little higher than that (3.82%) of the SDI-based method [19], and the EC value of our proposal (5.23%) is higher than that (2.97%) of the SI-based method [14].