期刊
REMOTE SENSING
卷 14, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/rs14184558
关键词
remote sensing; image segmentation; image classification; land use; land cover; Worldview-3
类别
资金
- European Research Council (ERC) under the European Union [679097]
- European Research Council (ERC) [679097] Funding Source: European Research Council (ERC)
This research focuses on the deep learning-based segmentation of VHR satellite images, specifically for LULC mapping. The study shows that the DeepLabv3+ architecture with a ResNeXt50 encoder achieves the best performance, providing highly accurate LULC maps.
Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.
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