SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation

• URL: http://arxiv.org/abs/2503.12404v1
• Date: Sun, 16 Mar 2025 08:11:11 GMT
• Title: SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
• Authors: Jianhao Yang, Wenshuo Yu, Yuanchao Lv, Jiance Sun, Bokang Sun, Mingyang Liu,
• Abstract summary: This work proposes an Edge-enhanced Labeling Network, called SAM2-ELNet, which incorporates a labeling module and an edge attention mechanism.<n>Our method uses the Hiera backbone of the pre-trained self-supervised large model segment anything model 2 (SAM2) as the encoder.<n>Results showed that the model trained with enhanced labels performed better and had a lower final loss, indicating closer alignment with the real data distribution.
• Score: 1.7906674529114799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing image segmentation is crucial for environmental monitoring, disaster assessment, and resource management, directly affecting the accuracy and efficiency of surface information extraction. The performance of existing supervised models in remote sensing image segmentation tasks highly depends on the quality of label data. However, current label data mainly relies on manual annotation, which comes with high time costs and is subject to subjective interference, resulting in distortion of label boundaries and often a loss of detail. To solve the above problems, our work proposes an Edge-enhanced Labeling Network, called SAM2-ELNet, which incorporates a labeling module and an edge attention mechanism. This model effectively addresses issues such as label detail loss, fragmentation, and inaccurate boundaries. Due to the scarcity of manually annotated remote sensing data, the feature extraction capabilities of traditional neural networks are limited. Our method uses the Hiera backbone of the pre-trained self-supervised large model segment anything model 2 (SAM2) as the encoder, achieves high-quality and efficient feature extraction even with small samples by fine-tuning on downstream tasks. This study compared the training effects of original and enhanced labels on the manually annotated Deep-SAR Oil Spill (SOS) dataset. Results showed that the model trained with enhanced labels performed better and had a lower final loss, indicating closer alignment with the real data distribution. Our work also explores the potential of extending the model into an efficient automatic annotation framework through generalization experiments, facilitating large-scale remote sensing image interpretation and intelligent recognition.

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