This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descriptive power over unimodal queries, either visual or textual. Various attributes can be modified by the textual part, such as shape, color, or context. A novel method fusing image-to-image and text-to-image similarity is introduced. We demonstrate that a vision-language model possesses sufficient descriptive power and no further learning step or training data are necessary. We present a new evaluation benchmark focused on color, context, density, existence, quantity, and shape modifications. Our work not only sets the state-of-the-art for this task, but also serves as a foundational step in addressing a gap in the field of remote sensing image retrieval. Code at: https://github.com/billpsomas/rscir

本文介绍了一种应用于遥感的组合图像检索方法，该方法允许通过图像示例和文本描述在大型图像库中进行查询，丰富了对单模态查询（无论是视觉还是文本）的描述能力。我们介绍了一种融合了图像-图像和文本-图像相似度的新方法，证明了视觉-语言模型具有足够的描述能力，无需进一步的学习步骤或训练数据。我们提出了一个新的评估基准，重点关注颜色、上下文、密度、存在性、数量和形状的修改。本工作不仅在此任务中取得了最新的技术成果，同时也填补了遥感图像检索领域的空白。

遥感图像的组合检索