Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/10/880 |
| _version_ | 1797575109893947392 |
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| author | Ahram Song |
| author_facet | Ahram Song |
| author_sort | Ahram Song |
| collection | DOAJ |
| description | Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily due to environmental factors such as atmospheric conditions and relief displacement. Unmanned aerial vehicle (UAV) imagery presents unique challenges, such as variations in object appearance due to UAV flight altitude and shadows in urban areas. This study analyzed the combined segmentation network (CSN) designed to train heterogeneous UAV datasets effectively for their segmentation performance across different data types. Results confirmed that CSN yielded high segmentation accuracy on specific classes and can be used on diverse data sources for UAV image segmentation. The main contributions of this study include analyzing the impact of CSN on segmentation accuracy, experimenting with structures with shared encoding layers to enhance segmentation accuracy, and investigating the influence of data types on segmentation accuracy. |
| first_indexed | 2024-03-10T21:31:39Z |
| format | Article |
| id | doaj.art-c1a73ca436d34c46bbecf859e899e0e6 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T21:31:39Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-c1a73ca436d34c46bbecf859e899e0e62023-11-19T15:17:25ZengMDPI AGAerospace2226-43102023-10-01101088010.3390/aerospace10100880Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle DatasetsAhram Song0Department of Location-Based Information System, Kyungpook National University, Gyeongsang-daero, Gyeongsangbuk-do, Sangju 2559, Republic of KoreaDeep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily due to environmental factors such as atmospheric conditions and relief displacement. Unmanned aerial vehicle (UAV) imagery presents unique challenges, such as variations in object appearance due to UAV flight altitude and shadows in urban areas. This study analyzed the combined segmentation network (CSN) designed to train heterogeneous UAV datasets effectively for their segmentation performance across different data types. Results confirmed that CSN yielded high segmentation accuracy on specific classes and can be used on diverse data sources for UAV image segmentation. The main contributions of this study include analyzing the impact of CSN on segmentation accuracy, experimenting with structures with shared encoding layers to enhance segmentation accuracy, and investigating the influence of data types on segmentation accuracy.https://www.mdpi.com/2226-4310/10/10/880unmanned aerial vehicle (UAV)datasetsemantic segmentationcombined segmentation networksemantic drone dataset (SDD)UAVid |
| spellingShingle | Ahram Song Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets Aerospace unmanned aerial vehicle (UAV) dataset semantic segmentation combined segmentation network semantic drone dataset (SDD) UAVid |
| title | Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets |
| title_full | Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets |
| title_fullStr | Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets |
| title_full_unstemmed | Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets |
| title_short | Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets |
| title_sort | deep learning based semantic segmentation of urban areas using heterogeneous unmanned aerial vehicle datasets |
| topic | unmanned aerial vehicle (UAV) dataset semantic segmentation combined segmentation network semantic drone dataset (SDD) UAVid |
| url | https://www.mdpi.com/2226-4310/10/10/880 |
| work_keys_str_mv | AT ahramsong deeplearningbasedsemanticsegmentationofurbanareasusingheterogeneousunmannedaerialvehicledatasets |