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

• URL: http://arxiv.org/abs/2503.12404v2
• Date: Sun, 21 Sep 2025 01:18:09 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: We introduce a novel label enhancement and automatic annotation framework, termed SAM2-ELNet.<n>Specifically, we employ the frozen Hiera backbone from the pretrained SAM2 as the encoder, while fine-tuning the adapter and decoder.<n>We design a series of experiments targeting resource-limited remote sensing tasks and evaluate our method on two datasets.
• Score: 2.5292915978887387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing image segmentation is crucial for environmental monitoring, disaster assessment, and resource management, but its performance largely depends on the quality of the dataset. Although several high-quality datasets are broadly accessible, data scarcity remains for specialized tasks like marine oil spill segmentation. Such tasks still rely on manual annotation, which is both time-consuming and influenced by subjective human factors. The segment anything model 2 (SAM2) has strong potential as an automatic annotation framework but struggles to perform effectively on heterogeneous, low-contrast remote sensing imagery. To address these challenges, we introduce a novel label enhancement and automatic annotation framework, termed SAM2-ELNet (Enhancement and Labeling Network). Specifically, we employ the frozen Hiera backbone from the pretrained SAM2 as the encoder, while fine-tuning the adapter and decoder for different remote sensing tasks. In addition, the proposed framework includes a label quality evaluator for filtering, ensuring the reliability of the generated labels. We design a series of experiments targeting resource-limited remote sensing tasks and evaluate our method on two datasets: the Deep-SAR Oil Spill (SOS) dataset with Synthetic Aperture Radar (SAR) imagery, and the CHN6-CUG Road dataset with Very High Resolution (VHR) optical imagery. The proposed framework can enhance coarse annotations and generate reliable training data under resource-limited conditions. Fine-tuned on only 30% of the training data, it generates automatically labeled data. A model trained solely on these achieves slightly lower performance than using the full original annotations, while greatly reducing labeling costs and offering a practical solution for large-scale remote sensing interpretation.

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