Prototype-CNN for Few-Shot Object Detection in Remote Sensing Images

Gong Cheng, Bowei Yan, Peizhen Shi, Ke Li, Xiwen Yao, Lei Guo, Junwei Han

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

Abstract

Recently, due to the excellent representation ability of convolutional neural networks (CNNs), object detection in remote sensing images has undergone remarkable development. However, when trained with a small number of samples, the performance of the object detectors drops sharply. In this article, we focus on the following three main challenges of few-shot object detection in remote sensing images: 1) since the sample number of novel classes is far less than base classes, object detectors would fail to quickly adapt to the features of novel classes, which would result in overfitting; 2) the scarcity of samples in novel classes leads to a sparse orientation space, while the objects in remote sensing images usually have arbitrary orientations; and 3) the distribution of object instances in remote sensing images is scattered and, therefore, it is hard to identify foreground objects from the complex background. To tackle these problems, we propose a simple yet effective method named prototype-CNN (P-CNN), which mainly consists of three parts: a prototype learning network (PLN) converting support images to class-aware prototypes, a prototype-guided region proposal network (P-G RPN) for better generation of region proposals, and a detector head extending the head of Faster region-based CNN (R-CNN) to further boost the performance. Comprehensive evaluations on the large-scale DIOR dataset demonstrate the effectiveness of our P-CNN. The source code is available at https://github.com/Ybowei/P-CNN.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
StatePublished - 2022

Keywords

  • Convolutional neural network (CNN)
  • few-shot object detection
  • prototype-CNN (P-CNN)
  • remote sensing images

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