| Title: | Tilt correction toward building detection of remote sensing images |
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| Authors: | ID Liu, Kang (Author)
ID Jiang, Zhiyu (Author)
ID Xu, Mingliang (Author)
ID Perc, Matjaž (Author)
ID Li, Xuelong (Author) |
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| Files: |  Liu-2021-Tilt_Correction_Toward_Building_Detec.pdf (8,62 MB)
MD5: D5F7D17CB050F91939E00D053E4F1688
 https://doi.org/10.1109/JSTARS.2021.3083481
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
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| Abstract: | 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. |
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| Keywords: | building detection, cost of building partition, deep neural network, remote sensing, tilt correction |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 16.03.2021 |
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| Article acceptance date: | 21.05.2021 |
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| Publication date: | 25.05.2021 |
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| Publisher: | Institute of Electrical and Electronics Engineers |
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| Year of publishing: | 2021 |
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| Number of pages: | Str. 5854-5866 |
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| Numbering: | Letn. 14 |
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| PID: | 20.500.12556/DKUM-90828  |
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| UDC: | 53 |
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| ISSN on article: | 1939-1404 |
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| COBISS.SI-ID: | 67635971 |
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| DOI: | 10.1109/JSTARS.2021.3083481 |
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| Publication date in DKUM: | 26.09.2024 |
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| Views: | 133 |
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| Downloads: | 9 |
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| Metadata: |    |
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| Categories: | Misc.
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