Date of Completion
shadow, land cover, remote sensing
Field of Study
Natural Resources: Land, Water, and Air
Doctor of Philosophy
High-resolution imagery is becoming increasingly available for use in land-cover mapping; however, previous studies have found that urban shadows cast by elevated features in urban environments can cause substantial errors in land-cover classifications. Detecting and restoring land-cover information within urban shadows can help improve accuracy in land-cover mapping. Although a considerable number of studies have been conducted on both shadow detection and shadow restoration, few studies have focused on a complete urban shadow correction workflow that combines shadow detection and restoration for the purpose of land-cover mapping using high-resolution aerial imagery across large geographic extents s. Thus, the goal of this research was to develop a semi-automated approach to detect urban shadows and classify land-cover information within shadow areas for high-resolution aerial imagery. The specific objectives of this research were to: 1) develop and evaluate approaches that integrate multiple shadow detection methods, 2) evaluate the robustness of integrated shadow detection methods for a variety of landscapes and different forest canopy conditions, and 3) develop and evaluate a shadow correction algorithm to improve land-cover classification within shadow areas. This research will be beneficial to the remote sensing community working with high-resolution imagery by allowing them to mitigate the errors caused by shadows in land-cover classification at broad geographic scales with a low degree of human intervention.
Lei, Qian, "Shadow Detection and Classification in High-resolution Aerial Imagery" (2020). Doctoral Dissertations. 2598.