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Self-supervised learning for remote sensing scene classification under the few shot scenario.

Najd Alosaimi1, Haikel Alhichri2, Yakoub Bazi2

  • 1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, 2454, Riyadh, 11451, Saudi Arabia. najd.alosaimi@gmail.com.

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This study introduces RS-FewShotSSL, a novel self-supervised learning method for remote sensing scene classification with limited labeled data. RS-FewShotSSL effectively learns discriminative features from unlabeled images, outperforming existing methods in few-shot scenarios.

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Scene classification in remote sensing (RS) faces challenges like complex scenes, overlapping classes, and insufficient labeled data.
  • Deep learning (DL) and convolutional neural networks (CNNs) are state-of-the-art but require extensive annotated data.
  • Self-Supervised Learning (SSL) offers a solution by learning from abundant unlabeled data, reducing labeling dependency.

Purpose of the Study:

  • To propose a deep SSL method, RS-FewShotSSL, for RS scene classification in few-shot scenarios (less than 20 labeled scenes per class).
  • To address the limitations of traditional DL methods that fail dramatically with scarce labeled data.
  • To develop an effective SSL approach that leverages large amounts of unlabeled RS images.

Main Methods:

  • RS-FewShotSSL employs an online and a target network with EfficientNet-B3 as the feature encoder backbone.
  • Pretext task: Cross-view contrastive learning on unlabeled images using geometric transformations to learn discriminative features.
  • Downstream task: Fine-tuning the online network on few labeled scenes after discarding the target network. Utilizes a novel architecture for training with both high- and low-resolution images to optimize batch sizes and feature learning.

Main Results:

  • RS-FewShotSSL demonstrated significant improvement on three public RS datasets compared to state-of-the-art methods (SimCLR, MoCo, BYOL, IDSSL).
  • The proposed method effectively learns from limited labeled data by pre-training on large unlabeled datasets.
  • The novel DL architecture allows for benefits from both large batch sizes (using low-resolution images during pre-training) and full image sizes (during fine-tuning).

Conclusions:

  • RS-FewShotSSL is a highly effective SSL method for remote sensing scene classification, particularly in few-shot learning scenarios.
  • The approach successfully mitigates the need for extensive labeled data by leveraging unlabeled imagery.
  • The method offers a promising direction for improving the efficiency and performance of RS scene classification systems.