Few-Shot Object Detection for Remote Sensing Images via Pseudo-Sample Generation and Feature Enhancement
Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. However, existing methods mainly focus on natural images and overlook the scale variations in remote sensing images, leading to feature confusion among foreground instances of different cl...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4477 |
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| Summary: | Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. However, existing methods mainly focus on natural images and overlook the scale variations in remote sensing images, leading to feature confusion among foreground instances of different classes. Additionally, since only a subset of instances are labeled in FSOD training data, the model might mistakenly treat unlabeled instances as background, leading to confusion between foreground features and background features, particularly those of novel classes. The preceding phenomenon indicates that severe feature confusion in remote sensing FSOD hampers the ability of the model to accurately classify and localize instances. To address these issues, this paper proposes a two-stage FSOD framework based on transfer learning via pseudo-sample generation and feature enhancement (PSGFE), including pseudo-sample generation module (PSGM) and feature enhancement module (FEM). The former reduces the feature confusion between foreground and background by generating pseudo-samples for unannotated background areas. The latter dynamically captures and enhances multi-scale features on the region of interest (ROI), and extracts unique core information for each class to eliminate the feature confusion among foreground instances of different classes. Our method has been validated on the optical remote sensing datasets DIOR and RSOD. It demonstrates superior performance compared to existing methods. |
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| ISSN: | 2076-3417 |