同时,也进行了细致的比较分析,将RSAM-Seg与原始架构以及在通用语义分割领域广泛采用的U-Net模型进行基准比较。 研究结果表明,通过利用先验信息的整合,RSAM-Seg在少样本学习场景中展现出了一定能力。此外,RSAM-Seg作为一个辅助注释工具具有潜力,提供了一种新的方法来促进数据集的创建,同时减轻相关成本。 在未来,主要...
为了解决这些局限性,我们提出了RSAM-Seg,一种专为遥感应用设计的基于SAM的新型深度学习模型。我们的模型包含两个关键组件:Adapter-Scale和Adapter-Feature模块。Adapter-Scale模块集成在视觉变换器(ViT)块中,通过可学习的变换增强模型的适应性,而Adapter-Feature模块则位于ViT块之间,通过结合任务特定信息生成图像信息提示。
RSAM-Seg: A SAM-Based Model with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation 来自 mdpi.com 喜欢 0 阅读量: 1 作者: Li Zhang 摘要: High-resolution remote sensing satellites have revolutionized remote sensing research, yet accurately segmenting specific targets from ...
In addition, RSAM-Seg maintains robust performance in few-shot learning scenarios, achieving an F1 score of 0.656 with only 1% of the training data and increasing to 0.815 with full data availability. Furthermore, RSAM-Seg exhibits the capability to detect missing areas within the ground truth...
RSAM-Seg introduces two key innovations: an automatic prompt generation mechanism that eliminates manual intervention and a domain-specific enhancement module optimized for remote sensing imagery characteristics. While the original SAM model demonstrates limited effectiveness in remote sensing applications, ...
Journal of Cellular Biochemistry
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