mlsanityData to 14 Oct 2026

Global-Local Detail Guided Transformer for Sea Ice Recognition in Optical Remote Sensing Images

arXiv 2405.13197 ↗First posted 21 May 2024cs.CVcited by 0

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Abstract (arXiv)

The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep learning based methods have been proposed and applied to segment and recognize sea ice regions. However, the diverse scales of sea ice areas, the zigzag and fine edge contours, and the difficulty in distinguishing different types of sea ice pose challenges to existing sea ice recognition models. In this paper, a Global-Local Detail Guided Transformer (GDGT) method is proposed for sea ice recognition in optical remote sensing images. In GDGT, a global-local feature fusiont mechanism is designed to fuse global structural correlation features and local spatial detail features. Furthermore, a detail-guided decoder is developed to retain more high-resolution detail information during feature reconstruction for improving the performance of sea ice recognition. Experiments on the produced sea ice dataset demonstrated the effectiveness and advancement of GDGT.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 21 May 2024.

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8 of 15 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 167
  4. 2020Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Imagescited by 59
  5. 2021A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Imagescited by 56
  6. 2018Fast End-to-End Trainable Guided Filtercited by 50
  7. 2021Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Imagescited by 28
  8. 2021ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Imagescited by 22

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