## 3 Experiments and Performance Assessment
### Qualitative Visual Sense Comparison  
Figures 6 and 7 respectively present the binary shadow detection results of test images Tripoli-1 and Tripoli-2 by the proposed _LSI_ shadow detection approach and five other comparative methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) in comparative experiments. Particularly, Figures 6a-e and 7a-e list shadow detection results by the proposed _LSI_ shadow detection approach in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ). Figures 6f-i and 7f-i illustrate shadow detection results by five other comparative methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]). Shadow detection results are usually intuitively evaluated through visual comparison [14; 20]. In order to evaluate the ability of different color spaces in decoupling chromaticity and luminance, shadow detection results are first compared through the qualitative visual sense comparison, respectively, which are processed by the proposed _LSI_ shadow detection approach in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), as presented in Figures 6a-e and 7a-e. In Figure 6a-e, shadow is correctly classified to a great extent. Specifically, most ground objects in nonshadow regions are well distinguished from shadow, such as bluish housetops (region A in Figure 6a-e), dark asphalt roads and bare areas (regions B1 and B2 in Figure 6a-e), grass and isolated vegetation (regions C1 and C2 in Figure 6a-e). Moreover, continuous shadow (region E in Figure 6a-e) and shadow containing highlight ground objects (regions F1 and F2 in Figure 6a-e) are also identified properly. Good coherence occurs among shadow detection results by the proposed _LSI_ shadow detection approach in these five invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ). Similarly, shadow detection results by the _LSI_ algorithm for the test image Tripoli-2 in Figure 7a-e also declare a good agreement between these shadow detection results and the corresponding reference image in Figure 5b. In Figure 7a-e, shadow is also specifically distinguished from typical ground objects, like greenish parts in the playground (region A in Figure 7a-e), asphalt roads and dark elements on tops of urban buildings (regions B1 and B2 in Figure 7a-e), as well as continuously distributed grass (region C in Figure 7a-e). Moreover, highlight shadow (region F in Figure 7a-e) is also outlined. Similar with shadow detection results for the test image Tripoli-1 in these invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), shadow detection results in Figure 7a-e also show no obvious  
Figure 5: Reference images for test images of Tripoli, Libya. (**a**) Tripoli-1. (**b**) Tripoli-2. Figure 6: Shadow detection results of various shadow detection algorithms for Tripoli-1. (**a**) HIS. (**b**) HSV. (**c**) CIELCh. (**d**) YCbCr. (**e**) YIQ. (**f**) MC3 [18]. (**g**) Normalized saturation-value index (NSVDI) [25]. (**h**) Logarithmic spectral ratio index (LSRI) [24]. (**i**) Shadow detector index (SDI) [26]. (**j**) Spectral ratio index (SRI) [20]. Figure 7: \(Cont\). Based on the good coherence among shadow detection results in these invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), shadow detection results by the proposed \(LSI\) approach for test images Tripoli-1 and Tripoli-2 in the HSV color space in Figures 6b and 7b are particularly selected for the comparison with shadow detection results by other five comparative methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) shown in Figures 6f-i and 7f-i. As described above, shadow is well distinguished from most typical ground objects in the shadow detection result for the test image Tripoli-1 by the proposed \(LSI\) approach, as shown in Figure 6b. In Figure 6f, the shadow detection result by MC3 also shows good shadow detection effect on grass and large continuous shadow.