DIVERSITY MEASUREMENT-BASED META-LEARNING FOR FEW-SHOT OBJECT DETECTION OF REMOTE SENSING IMAGES

Most object detection methods based on deep learning require large amounts of labeled data and can detect only the categories in the training set. Such issues significantly limit applications in remote sensing scenarios where it usually needs to recognize novel, unseen objects given very few training examples. To address these limitations, a novel meta-learning-based object detection method using Faster R-CNN framework is proposed for optical remote sensing image. Specifically, a diversity measurement module is proposed to measure diversity information between support images and query images on base classes so as to acquire more meta-knowledge. Experiments on DIOR dataset demonstrate our method has achieved superior performance than state-of-the-art meta-learning detection models in the field of remote sensing.