Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.