A Multimodal Visible-SAR Dataset for Airport Detection in Remote Sensing Imagery

This dataset is based on Google Earth visible light images and Gaofen-3 (GF-3) synthetic aperture radar (SAR) images, and constructs a visible light SAR remote sensing multimodal target detection dataset focusing on airport targets nationwide. The dataset consists of two parts: image data and annotation data. The image data is divided into visible light modal data and SAR modal data, and the annotation file data includes VOC rotation box annotation file format and YOLO horizontal box annotation file format. In the process of constructing the dataset, follow standardized methods for data collection, processing, and annotation. The dataset has the characteristics of pixel level alignment, large spatial span, large scale span, rich scene diversity, and small targets, which can meet the training needs of mainstream deep learning models. This dataset is the first publicly released visible light SAR spatially aligned remote sensing multimodal object detection dataset, providing important basic data resources for researchers in related fields and having practical application value for promoting research in the field of remote sensing multimodal object detection.