SAR-TEXT：基于SAR-Narrator构建的大规模SAR图像-文本数据集及面向下游任务的渐进式学习策略
SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and A Progressive Learning Strategy for Downstream Tasks
AI总结：
针对SAR图像-文本数据集匮乏的问题，研究团队构建了含13万余对样本的大规模SAR-TEXT数据集，设计SAR-Narrator框架生成文本描述，并验证了其在三类视觉语言任务上的性能提升效果。
摘要：
近年来，视觉语言模型（VLM）在遥感领域取得了显著突破。合成孔径雷达（Synthetic Aperture Radar, SAR）影像凭借其全天候工作能力在遥感中至关重要，但缺乏大规模、高质量的SAR图像-文本数据集阻碍了其语义理解的发展。本文构建了SAR-TEXT这一包含超过13万对SAR图像-文本对的大规模高质量数据集。为构建该数据集，我们设计了SAR-Narrator框架，通过多阶段策略为SAR图像生成文本描述。为验证SAR-TEXT数据集的有效性，我们在三类典型视觉-语言任务上开展实验：图像-文本检索、图像字幕生成以及视觉问答（VQA）。具体而言，我们在SAR-TEXT上构建了三个代表性模型：SAR-RS-CLIP、SAR-RS-CoCa和SAR-GPT。其中SAR-RS-CLIP在检索性能上取得显著提升，在OSdataset_512和HRSID测试集上的平均召回率分别提升12.97%和10.0%。在字幕生成任务中，SAR-RS-CoCa在BLEU-4、SPICE和CIDEr指标上相比原始CoCa模型取得显著提升。在VQA任务中，SAR-GPT在多个SAR-VQA数据集上表现优于基线模型和单阶段模型，展现出更强的语义理解与推理能力，定性结果也进一步证实了这一点。值得注意的是，SAR-Narrator作为一款灵活的字幕生成工具，可被学界便捷用于构建更大规模的SAR图像-文本数据集。所有代码、预训练模型以及SAR-Text数据集均公开于：https://github.com/YiguoHe/SAR-TEXT。
英文摘要：
Vision Language Models (VLMs) have achieved remarkable breakthroughs in the field of remote sensing in recent years. Synthetic Aperture Radar (SAR) imagery, with its all-weather capability, is essential in remote sensing, yet the lack of large-scale, high-quality SAR image-text datasets hinders its semantic understanding. In this paper, we construct SAR-TEXT, a large-scale and high-quality dataset consisting of over 130,000 SAR image-text pairs. To construct the SAR-TEXT dataset, we design the SAR-Narrator framework, which generates textual descriptions for SAR images through a multi-stage strategy. To verify the effectiveness of the SAR-TEXT dataset, we conduct experiments on three typical vision-language tasks: image-text retrieval, image captioning, and visual question answering (VQA). Specifically, we construct three representative models on SAR-TEXT: SAR-RS-CLIP, SAR-RS-CoCa, and SAR-GPT. SAR-RS-CLIP achieves notable improvements in retrieval performance, boosting average recall by 12.97% and 10.0% on the OSdataset_512 and HRSID test sets, respectively. In the captioning task, SAR-RS-CoCa achieves significant improvements over the original CoCa models in terms of BLEU-4, SPICE, and CIDEr scores. In the VQA task, SAR-GPT outperforms baseline and single-stage models on multiple SAR-VQA datasets, demonstrating stronger semantic understanding and reasoning ability, as further confirmed by qualitative results. It is worth noting that, as a flexible captioning tool, SAR-Narrator can be readily adopted by the community to construct larger-scale SAR image-text datasets. All code, pretrained models, and the SAR-Text dataset are publicly available at: https://github.com/YiguoHe/SAR-TEXT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
