## 3 Experiments and Performance Assessment
### Qualitative Visual Sense Comparison
However, many parts of bluish housetops (region A in Figure 6f) and partial dark asphalt roads (region B1 in Figure 6f) are wrongly classified as shadow in Figure 6f. Moreover, in Figure 6g, the shadow detection result by NSVDI show more serious misclassification of bluish housetops and dark asphalt roads, although big shadow regions are detected. Similarly, most large shadow regions are well detected by SDI and SRI, like building shadow, as shown in Figure 6i,j. However, bluish housetops and dark asphalt roads are still mostly wrongly identified as shadow. Figure 7: Shadow detection results of various shadow detection algorithms for Tripoli-2. (**a**) HIS. (**b**) HSV. (**c**) CIELCh. (**d**) YCbCr. (**e**) YIQ. (**f**) MC3. (**g**) NSVDI. (**h**) LSRI. (**i**) SDI. (**j**) SRI. Moreover, parts of grass and isolated vegetation are also identified as shadow by SDI and SRI (regions C1 and C2 in Figure 6i). Different from shadow detection results in Figure 6f,g,i,j, the nonshadow misclassification problem is mostly avoided in the shadow detection result by LSRI, as shown in Figure 6h. However, shadow is not always detected completely (region E in Figure 6h), and highlight parts in shadow regions are partially omitted (regions F1 and F2 in Figure 6h), which reveals that LSRI can not deliver a excellent shadow detection performance. Compared with shadow detection results by these five comparative algorithms (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) for the test image Tripoli-1, the shadow detection result by the proposed _LSI_ algorithm alleviates problems of shadow omission and typical nonshadow misclassification to a greater extent. Accordingly, a better visual sense is acquired by _LSI_. As shown in Figure 7b, shadow is effectively distinguished from bluish parts of the artificial playground (region A in Figure 7b), dark asphalt roads and tops of buildings (regions B1 and B2 in Figure 7b) and continuous distributed greenish grass (region C in Figure 7b). Moreover, highlight parts in shadow areas are also correctly identified (region F in Figure 7b). Shadow is also well separated from grass by MC3 as shown in Figure 7f. However, there are still too many nonshadow regions misclassified, such as most bluish parts in the playground (region A in Figure 7f) and dark asphalt roads and tops of buildings (regions B1 and B2 in Figure 7f). Similarly, although most shadow regions are well identified by NSVDI, SDI and SRI, the nonshadow misclassification problem is still obvious in Figure 7g,i,j, like bluish parts of the playground (region A in Figure 7g,i,j), dark asphalt roads (region B1 in Figure 7g,i,j) and greenish grass (region C in Figure 7g,i,j). By contrast, in Figure 7h, most shadow regions and nonshadow regions are well separated, like bluish parts of the playground (region A in Figure 7h), dark asphalt roads and tops of buildings (regions B1 and B2 in Figure 7h) and continuously distributed greenish grass (region C in Figure 7h), which shows that a relatively good detection effect is achieved by LSRI. Satisfactory overall shadow detection effect is obtained in Figure 7h, even though parts of the highlighted shadow are still omitted. As can be observed in Figure 7b,f-h, results by _LSI_ and LSRI show a better visual sense. In general, compared with shadow detection results for test images Tripoli-1 and Tripoli-2 by other five shadow detection methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]. ), the proposed _LSI_ approach effectively distinguish shadow from several typical nonshadow (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification), and well detect most highlighted parts of shadow. A conclusion can be drawn that the proposed _LSI_ algorithm further resolves problems of shadow omission and typical nonshadow misclassification, and delivers a better visual sense.