Semantic Segmentation of Drone Images Based on Combined Segmentation Network Using Multiple Open Datasets

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Abstract

This study proposed and validated a combined segmentation network (CSN) designed to effectively train on multiple drone image datasets and enhance the accuracy of semantic segmentation. CSN shares the entire encoding domain to accommodate the diversity of three drone datasets, while the decoding domains are trained independently. During training, the segmentation accuracy of CSN was lower compared to U-Net and the pyramid scene parsing network (PSPNet) on single datasets because it considers loss values for all datasets simultaneously. However, when applied to domestic autonomous drone images, CSN demonstrated the ability to classify pixels into appropriate classes without requiring additional training, outperforming PSPNet. This research suggests that CSN can serve as a valuable tool for effectively training on diverse drone image datasets and improving object recognition accuracy in new regions.

Original languageEnglish
Pages (from-to)967-978
Number of pages12
JournalKorean Journal of Remote Sensing
Volume39
Issue number5-3
DOIs
StatePublished - 2023

Keywords

  • Combined segmentation network
  • Deep learning
  • Drone image
  • Semantic segmentation

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