Reconstruction Guided Few-shot Network For Remote Sensing Image Classification

2026-01-12

AI Summary

This work addresses the challenges of few-shot classification in remote sensing imagery, where labeled data are scarce and land cover exhibits high variability. To this end, the authors propose a Reconstruction-Guided Few-Shot Network (RGFS-Net), which integrates masked image reconstruction as an auxiliary task within a few-shot learning framework to encourage the model to learn semantically rich and spatially consistent feature representations. Notably, RGFS-Net requires no modification to the backbone architecture and is compatible with standard convolutional networks. By enforcing feature consistency constraints, the method enhances both retention of knowledge from seen classes and generalization to unseen classes. Experimental results on the EuroSAT and PatternNet datasets demonstrate that RGFS-Net consistently outperforms existing baselines under both 1-shot and 5-shot settings.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Few-shot remote sensing image classification is challenging due to limited labeled samples and high variability in land-cover types. We propose a reconstruction-guided few-shot network (RGFS-Net) that enhances generalization to unseen classes while preserving consistency for seen categories. Our method incorporates a masked image reconstruction task, where parts of the input are occluded and reconstructed to encourage semantically rich feature learning. This auxiliary task strengthens spatial understanding and improves class discrimination under low-data settings. We evaluated the efficacy of EuroSAT and PatternNet datasets under 1-shot and 5-shot protocols, our approach consistently outperforms existing baselines. The proposed method is simple, effective, and compatible with standard backbones, offering a robust solution for few-shot remote sensing classification. Codes are available at https://github.com/stark0908/RGFS.

Problem

• few-shot learning
• remote sensing image classification
• limited labeled samples
• land-cover variability
• image classification

Innovation

• few-shot learning
• masked image reconstruction
• remote sensing image classification
• feature learning
• generalization