Scholarlink

Image segmentation at the pixel level is a time-consuming and difficult task in computer vision and image processing. Aerial (satellite/drone) photo segmentation is considered in this paper. Data from high-resolution remote sensing has enabled new applications such as more detailed per-pixel object classification. U-Net with VGG16 has made segmentation and categorization of images much more efficient and intelligent. U-Net models with pre-trained VGG16 backbones perform best across all tested scenarios. Adding the near-infrared band improves prediction results slightly compared with using RGB image bands alone. The ability to transfer images between sensors, especially between satellites and aerial images, could be improved through train-time enhancement and contrast enhancement. Further improving performance could be achieved by adding noisy training data from free online resources.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Real-Time Aerial Semantic Segmentation System Based on U-Net Deep Learning Using Drone Images

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Muhammet Tahir Güneşer,
  • Chihat Şeker,
  • Mohammed Ayad Alkhafaji

摘要

Image segmentation at the pixel level is a time-consuming and difficult task in computer vision and image processing. Aerial (satellite/drone) photo segmentation is considered in this paper. Data from high-resolution remote sensing has enabled new applications such as more detailed per-pixel object classification. U-Net with VGG16 has made segmentation and categorization of images much more efficient and intelligent. U-Net models with pre-trained VGG16 backbones perform best across all tested scenarios. Adding the near-infrared band improves prediction results slightly compared with using RGB image bands alone. The ability to transfer images between sensors, especially between satellites and aerial images, could be improved through train-time enhancement and contrast enhancement. Further improving performance could be achieved by adding noisy training data from free online resources.