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An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals

Wenxuan Zhang, Peng Hu

TL;DR

This work tackles the challenge of detecting fine-grained weather-induced effects on satellite ground terminals under limited data. It introduces a feature-based transfer learning pipeline that combines YOLACT-based background removal with a ResNet50+FC classifier, optimized by the objective $L_{overall} = L(y, \hat{y}) + L_{YOLACT}$ and guided by domain alignment via $MMD(D_S, D_T)$. Through extensive data preparation (including external weather datasets and synthetic augmentation) and rigorous comparisons with state-of-the-art detectors, the method achieves higher accuracy and mAP than baselines in both binary and multiclass settings, while demonstrating robust generalization to real-world conditions. The approach offers a practical path toward reliable satellite Internet in remote regions by enabling efficient, data-frugal detection of weather effects on ground-terminal components, with potential extension to other ground-based sensing tasks.

Abstract

The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.

An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals

TL;DR

This work tackles the challenge of detecting fine-grained weather-induced effects on satellite ground terminals under limited data. It introduces a feature-based transfer learning pipeline that combines YOLACT-based background removal with a ResNet50+FC classifier, optimized by the objective and guided by domain alignment via . Through extensive data preparation (including external weather datasets and synthetic augmentation) and rigorous comparisons with state-of-the-art detectors, the method achieves higher accuracy and mAP than baselines in both binary and multiclass settings, while demonstrating robust generalization to real-world conditions. The approach offers a practical path toward reliable satellite Internet in remote regions by enabling efficient, data-frugal detection of weather effects on ground-terminal components, with potential extension to other ground-based sensing tasks.

Abstract

The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.

Paper Structure

This paper contains 19 sections, 5 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Overview
  4. Pre-Processing: Background Removal Using YOLACT
  5. Proposed TL Method
  6. Model Architecture
  7. Data Preparation
  8. Evaluation
  9. Evaluation Metrics
  10. Experimental Setup and Implementation Details
  11. Experimental Results
  12. Initial Scenario
  13. Extended Scenario
  14. Discussion of Model Superiority
  15. Generalizability to Real-World Scenarios
  16. ...and 4 more sections

Figures (3)

  • Figure 1: The process and architecture of the proposed transfer learning method.
  • Figure 2: A process of data generation. Satellite dishes from original images are segmented using YOLACT and combined with various weather condition backgrounds to generate a diverse training images.
  • Figure 3: Loss curve comparison across epochs for the proposed model and other models. The results are grouped by the "Initial" and "Extended" scenarios.