A two-stage semi-supervised object detection method for SAR images with missing labels based on meta pseudo-labels

Recently, deep learning has been applied to analyze satellite images. However, applying fully supervised object detection (FSOD) is impractical because it is challenging to detect and annotate objects that are relatively small in high-resolution satellite images. In addition, most data owned by public institutions, such as military reconnaissance videos, do not contain sufficient object class label information. Therefore, the application of semi-supervised objection detection (SSOD) is more practical. The SSOD performance is often determined by generated pseudo-labels. Therefore, this study proposes a 2-stage SSOD model to generate accurate pseudo-labels. In the first stage, annotations, including the location information of unlabeled data, are detected using a modified faster R-CNN model. In the second stage, pseudo-labels of objects are additionally suggested through the meta pseudo-label method. Finally, reliable pseudo-labels are generated by comparing and combining the pseudo-labels generated in each stage, improving the SSOD model performance. Extensive experiments were conducted using the xView3 dataset provided by the U.S. Defense Innovation Unit (DIU). The proposed method performed approximately 11% better than the FSOD model, which does not learn pseudo-labels for datasets with missing labels according to the evaluation metrics proposed by the DIU covering both object detection and classification performance.