## 3 Experiments and Performance Assessment
### Quantitative Evaluation
Accordingly, the time consumption is respectively summarized for shadow detection by the _LSI_ algorithm in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) and five comparative algorithms (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) for test image Tripoli-1 and Tripoli-2, as presented in Table 3. As can be observed in Table 3, time consumption values of the _LSI_ algorithm are relatively small for the shadow detection in these invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) for test images Tripoli-1 and Tripoli-2 because of the simple computation of these invariant color spaces except for the time consumption of shadow detection in the CIELCh color space due to its complex calculation from the RGB color space. Particularly, the least time is consumed for shadow detection in the HSV color space by the proposed _LSI_ algorithm for both test images Tripoli-1 and Tripoli-2. Hence, the proposed _LSI_ shadow detection algorithm delivers the most timesaving performance in the HSV color space. Considering the excellent and stable performance in these invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) and the most timesaving performance of the proposed _LSI_ algorithm in the HSV color space, for the sake of simplicity, shadow detection performance comparison is particularly conducted between shadow detection results by the _LSI_ algorithm in the HSV color space and five comparative shadow detection algorithms (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) for both test images Tripoli-1 and Tripoli-2. For the test image Tripoli-1, a higher value of the overall accuracy (over 92%) is acquired for the shadow detection result by the proposed _LSI_ algorithm in the HSV color space, compared with the overall accuracy of the result by other five contrast methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]). Although relatively high values of the overall accuracy are also obtained by \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Used (ms)** & **Color Space** & **Tripoli-1** & **Tripoli-2** \\ \hline \multirow{4}{*}{_LSI_} & HIS & 23.22 & 9.90 \\  & HSV & 16.72 & 6.32 \\  & CIELCh & 90.73 & 54.86 \\  & YCbCr & 23.13 & 13.41 \\  & YIQ & 29.30 & 12.93 \\ \hline MC3 & CIC2C3 & 18.24 & 10.94 \\ NSVDI & HSV & 21.48 & 14.09 \\ LSRI & CIELCh & 620.41 & 388.29 \\ SDI & _NIR_-RGB & 18.40 & 9.34 \\ SRI & HIS & 94.54 & 23.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time consumption of various shadow detection algorithms for test images Tripoli-1 and Tripoli-2. MC3 and LSRI, an obvious difference (about 3%) is also found compared with that of the proposed _LSI_ approach, which indicates that the proposed _LSI_ method performs better for the shadow detection of the test image Tripoli-1. In addition, not only relatively low values of the committed error and the omitted error but also high values of the shadow user's accuracy are acquired by MC3, which reveals that the MC3 method performs relatively well for shadow detection of the test image Tripoli-1. However, even though relatively high values of the shadow producer's accuracy and the nonshadow user's accuracy, as well as relatively low values of the omitted error are obtained by NSVDI, SDI and SRI, both relatively low overall accuracy and high committed error still obstacle the effective shadow detection performance for the test image Tripoli-1, which indicates the poor performance of NSVDI, SDI and SRI in effectively detecting shadow of the test image Tripoli-1. Therefore, there is still further study for NSVDI, SDI and SRI in detecting shadows of HSR satellite images. By contrast, relatively high overall accuracy and low committed error are acquired by LSRI for the test image Tripoli-1, revealing that the LSRI method performs well in correctly distinguishing shadow against easily-confused nonshadow for the test image Tripoli-1.