Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images | IEEE Journals & Magazine | IEEE Xplore

Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images


Abstract:

Fine-grained ship detection in remote sensing images (RSIs) depends heavily on numerous training data with expensive manual annotations. Learning novel ship categories fr...Show More

Abstract:

Fine-grained ship detection in remote sensing images (RSIs) depends heavily on numerous training data with expensive manual annotations. Learning novel ship categories from very few labeled samples and without forgetting the learned knowledge of seen categories is important to real-world applications. In this letter, we formulate fine-grained ship detection in RSIs as a problem of generalized few-shot object detection (G-FSOD). Existing methods often neglect the structured information in ship taxonomy, and thus result in mutually exclusive representations between base and novel classes and hinder the transfer of the learned knowledge to the novel concepts under the few-shot settings. To handle this problem, we propose to incorporate the inherent hierarchical taxonomy in ship classes into the generalized few-shot ship detection to leverage the shared knowledge among base and novel classes. In particular, a ship detector is trained based on the coarsest class labels and a multitask classification network is built to distinguish various ships at both coarse and fine-grained levels on base classes, which leads to a generalized ship representation between base classes to novel classes. To build the classifier of novel classes, a prototype bank is constructed with the few-shot samples of novel classes, without the wreck of the feature extractor so as to maintain the performance on base classes. Extensive experiments on two large-scale ship detection datasets demonstrate the effectiveness of our method against state-of-the-art methods.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 21)
Article Sequence Number: 6013005
Date of Publication: 22 July 2024

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