| dc.contributor | 地政系 | |
| dc.creator (作者) | 邱式鴻 | |
| dc.creator (作者) | Lin, Chia-Ying;Chio, Shih-Hong | |
| dc.date (日期) | 2026-05 | |
| dc.date.accessioned | 7-May-2026 16:22:12 (UTC+8) | - |
| dc.date.available | 7-May-2026 16:22:12 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-May-2026 16:22:12 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=182383 | - |
| dc.description.abstract (摘要) | Compared with conventional orthoimages that are geometrically corrected only for terrain relief, true orthoimages incorporate object height information to eliminate building displacement and leaning effects, making them particularly suitable for high-density urban environments. Nevertheless, extensive shadows cast by tall buildings reduce spectral reflectance and increase land-cover ambiguity, thereby complicating image interpretation and analysis. To improve shadow detection accuracy in urban true orthoimages, this study proposes an automated deep learning framework integrating multispectral and height information. The Xinyi District of Taipei City, characterized by dense high-rise buildings, was selected as the experimental area. A Res-UNet architecture was adopted, in which the encoder was constructed based on a residual network to alleviate gradient degradation during deep training, while the decoder employed a U-Net structure with skip connections to preserve spatial details and shadow boundaries. In addition, near-infrared (NIR) bands and a Digital Height Model (DHM) were incorporated into the input features, enabling the model to simultaneously learn spectral attenuation patterns and height-induced occlusion relationships. Experimental results indicate that the proposed approach achieves an Intersection over Union (IoU) of 0.9447 and an F1-score of 0.9715 on validation data, with an overall accuracy of 0.9831. These findings suggest that integrating multispectral and geometric information within a deep learning framework effectively enhances shadow detection performance in high-density urban environments. | |
| dc.format.extent | 30568 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | International Symposium on Remote Sensing 2026, Remote Sensing Society of Japan (RSSJ) | |
| dc.subject (關鍵詞) | True orthoimages; Urban shadow detection; Deep learning; Res-UNet; Multispectral information; Digital Height Model | |
| dc.title (題名) | Deep Learning for Urban Shadow Detection in True Orthoimages with Multispectral and Height Information | |
| dc.type (資料類型) | conference | |
