20.500.12556/DKUM-90828 Tilt correction toward building detection of remote sensing images Building detection is a crucial task in the field of remote sensing, which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned. This is not beneficial to preserve semantic completeness of the building objects. Motivated by the above fact, we first propose a framework for detecting objects in a large-size image, particularly for building detection. The framework mainly consists of two phases. In the first phase, we particularly propose a tilt correction (TC) algorithm, which contains three steps: texture mapping, tilt angle assessment, and image rotation. In the second phase, building detection is performed with object detectors, especially deep-neural-network-based methods. Last but not least, the detection results will be inversely mapped to the original large-size image. Furthermore, a challenging dataset named Aerial Image Building Detection is contributed for the public research. To evaluate the TC method, we also define an evaluation metric to compute the cost of building partition. The experimental results demonstrate the effects of the proposed method for building detection. building detection cost of building partition deep neural network remote sensing tilt correction detekcija zgradb cena parceliranja zgradb globoka nevronska mreža oddaljeno zaznavanje popravek nagiba true false true Institute of Electrical and Electronics Engineers Angleški jezik Slovenski jezik Znanstveno delo 2024-09-26 08:48:54 2024-09-26 09:14:47 2024-09-28 03:21:55 0000-00-00 00:00:00 2021 0 0 Str. 5854-5866 Letn. 14 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-03-16 2021-05-21 2021-05-25 53 67635971 10.1109/JSTARS.2021.3083481 1939-1404 Liu-2021-Tilt_Correction_Toward_Building_Detec.pdf Liu-2021-Tilt_Correction_Toward_Building_Detec.pdf 1 D5F7D17CB050F91939E00D053E4F1688 8fc0fddfec5188bd04a302b778425c681961f3f4a90c30903461f6ce5ec2d05a 5f30bdaf-7bd5-11ef-8e3e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=185915 https://doi.org/10.1109/JSTARS.2021.3083481 0 d2066a97-7bd5-11ef-8e3e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=185916 Fakulteta za naravoslovje in matematiko 0 0 0