Multi-Label Classification with Single Positive Label for Remote Sensing Image

In the field of Remote Sensing Scene Classification (RSSC), multi-label classification has become necessary. However, the creation of a multi-label dataset is a laborious process due to the higher annotation costs compared to multi-class classification. In this study, we conducted a pioneering experiment in the context of partial-label classification on remote sensing datasets and aim to discuss the differences and limitations. In partial-label classification, each image is assigned some "positive" labels, which means it is annotated, and other "unknown" labels which are not determined as positive or negative. Consequently, the model is trained with limited information. We evaluated the classification performance on MLRSNet and AID multilabel datasets, using the method and loss functions that have shown excellent performance in previous studies on ground-level view datasets. Our code is available at https://github.com/Kf-7070/ IGARSS2023_partial_label.