## 4 Discussion
### Subjective Assessment
#### 4.1.3 Assessment of Test Image C  
To further validate the effectiveness of our proposed shadow detection approach, we compared the performance of our developed method to other investigated shadow detection methods with test image C. The test image C, as shown in Figure 7a, includes diverse land cover types (vegetation regions both in large area and individually, roads, lawns, bare soil, buildings in various profiles, etc.) and shadow regions with various irregular shapes. Under this complex scene, the resulting images produced by the SDI-based method [19] shown in Figure 7g and our proposed shadow detection approach, shown in Figure 7h, achieved the closest appearances to the reference image, as shown in Figure 7b, compared to other shadow detection methods previously mentioned as shown in Figure 7c-h. When the resulting images were individually compared with the reference image in Figure 7b, the resulting image produced by the SRI-based method [11] shown in Figure 7c generally differentiated shadow regions from roads, lawns, bare soil, and most buildings. However, a vast majority of the vegetation was wrongly classified as shadow regions as this method is sensitive to bluish or greenish properties which are often presented by vegetation pixels. Similar to Figure 6d, the resulting image produced by the NSVDI-based method [18] shown in Figure 7d generally identified shadow regions from most roads and buildings, whereas substantially all of the vegetation pixels were still misclassified as shadows because of the lower saturation values of these objects in this complex scene. Also, dark terrace roofs, lawns and bare soil were misclassified as shadows. Furthermore, the misclassification of lawns and bare soil as shadows were overcome to some extent in the resulting image produced by the MC3-based method [18], shown in Figure 7e. Though most shadow regions were reasonably outlined, many small true shadow regions actually shown in Figure 7a were omitted in the resulting image shown in Figure 7e. Additionally, this technique failed to correctly recognize shadow pixels against roads and parts of buildings, and did not accurately preserve contours of shadow regions. Similarly, the resulting image produced by the SI-based method [14], shown in Figure 7f, generally delineated the outlines of most large shadow regions. However, this method still omitted most small shadow regions and failed to preserve the shapes of shadow regions, because pixel values on the boundary between shadow and nonshadow regions cannot be accurately highlighted with this method. Additionally, the resulting image produced by the SDI-based method [19], shown in Figure 7g, presented quite good performance in terms of shadow detection. Shadow regions were correctly identified in diverse scenes, mainly including a majority of vegetation regions, roads, lawns, and bare soil. Besides, contours of shadow regions were also well persevered. Nevertheless, the small shadow omission problem remains an issue to explore. In terms of our proposed approach, the resulting image shown in Figure 7h is closer in appearance to the reference image shown in Figure 7b compared with the other investigated comparable shadow detection methods. Even though a small amount of small shadow regions within vegetation regions was omitted, our approach performed well in correctly detecting shadow regions in various shapes. Also, shadow regions were well separated apart from most nonshadow regions, especially for vegetation. Therefore, our proposed approach is an improvement in terms of the problems posed by both shadow region omission and vegetation misclassification, compared with other investigated comparable shadow detection techniques.