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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 14 Sayı: 3

A Hybrid Framework Based on Transfer Learning for Seasonal Classification of Satellite Images

Uydu Görüntülerinin Mevsimsel Sınıflandırılması için Transfer Öğrenmeye Dayalı Hibrit Çerçeve

Eyyüp Yıldız(ERZINCAN BINALI YILDIRIM UNIVERSITY)0000-0002-7051-3368
Özge Aslan Yıldız(ERZINCAN BINALI YILDIRIM UNIVERSITY)0000-0001-7688-9326
Pages
1710–1723
Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that combining pretrained representations and fine-tuning improved seasonal classification in satellite imagery.

📌 3 Key Takeaways

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Objective & Hypothesis:Seasonal classification from satellite imagery is a crucial remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns.
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Key Findings & Data:This study proposes a hybrid framework based on transfer learning for seasonal classification using monthly satellite imagery from all 81 provinces of Türkiye.
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Conclusion & Impact:The results demonstrate that combining pretrained representations and fine-tuning improved seasonal classification in satellite imagery.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Transfer öğrenme · Ön eğitilmiş modeller · Makine öğrenmesi sınıflandırması

Abstract

Seasonal classification from satellite imagery is a crucial remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns. However, limited labeled data, regional variability, and the high computational cost of training large-scale networks from scratch make developing generalizable models for this task challenging. This study proposes a hybrid framework based on transfer learning for seasonal classification using monthly satellite imagery from all 81 provinces of Türkiye. Eight ImageNet pretrained deep learning models were evaluated as feature extractors, and the extracted representations were evaluated using seven machine learning classifiers. The selection of feature extractors and classifiers was performed using 10-fold cross-validation repeated five times. Among the feature extractor models, the combination of ConvNeXt and Multi-Layer Perceptron produced the best result with a mean accuracy of 0.698 and a mean F1-score of 0.696. As a result of the comparative analysis, ConvNeXt, Vision Transformer, and Swin Transformer were selected as the three most successful feature extractors, and Multi-Layer Perceptron was selected as the final classifier. The hybrid model, constructed by concatenating the features of these three selected feature extractors, achieved a mean accuracy of 0.725 and a mean F1-score of 0.724. After fine-tuning, the proposed hybrid model achieved the highest performance with a mean accuracy of 0.740 and a mean F1-score of 0.742. The results demonstrate that combining pretrained representations and fine-tuning improved seasonal classification in satellite imagery. The proposed framework offers an effective and computationally practical approach for seasonal classification, providing a promising foundation for future environmental monitoring and agricultural remote sensing applications.

Özet

Uydu görüntülerinden mevsimsel sınıflandırma, bitki örtüsü dinamiklerini, tarımsal süreçleri, çevresel değişimi ve iklimle ilgili mekânsal desenleri izlemek için önemli bir uzaktan algılama görevidir. Bununla birlikte, sınırlı sayıda etiketli veri, bölgesel değişkenlik ve büyük ölçekli ağların sıfırdan eğitilmesinin yüksek hesaplama maliyeti, bu görev için genelleştirilebilir modeller oluşturmayı zorlaştırmaktadır. Bu çalışma, Türkiye'nin 81 ilinin tamamından elde edilen aylık uydu görüntülerini kullanarak mevsimsel sınıflandırma için transfer öğrenmeye dayalı hibrit bir çerçeve önermektedir. Sekiz adet ImageNet ön eğitilmiş derin öğrenme modeli özellik çıkarıcı olarak değerlendirilmiş ve çıkarılan temsiller yedi makine öğrenmesi sınıflandırıcısı kullanılarak sınıflandırılmıştır. Özellik çıkarıcıların ve sınıflandırıcıların seçimi, beş kez tekrarlanan 10 katlı çapraz doğrulama kullanılarak gerçekleştirilmiştir. Özellik çıkarıcı modeller arasında, ConvNeXt ve çok katmanlı algılayıcı kombinasyonu, ortalama 0,698 doğruluk ve ortalama 0,696 F1 puanı ile en iyi sonucu vermiştir. Karşılaştırmalı analiz sonucunda, ConvNeXt, Vision Transformer ve Swin Transformer en başarılı üç özellik çıkarıcı olarak seçilmiş ve çok katmanlı algılayıcı nihai sınıflandırıcı olarak belirlenmiştir. Seçilen bu üç özellik çıkarıcının özelliklerinin birleştirilmesiyle oluşturulan hibrit model, ortalama 0,725 doğruluk ve ortalama 0,724 F1 puanı elde etmiştir. İnce ayardan sonra, önerilen hibrit model, ortalama 0,740 doğruluk ve ortalama 0,742 F1 puanı ile en yüksek performansı göstermiştir. Sonuçlar, önceden eğitilmiş temsillerin birleştirilmesi ve ince ayarın, uydu görüntülerinde mevsimsel sınıflandırmayı iyileştirdiğini göstermektedir. Önerilen çerçeve, mevsimsel sınıflandırma için etkili ve hesaplama açısından pratik bir yaklaşım sunarak, gelecekteki çevresel izleme ve tarımsal uzaktan algılama uygulamaları için umut vadeden bir temel sağlamaktadır.

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Cite This Article

Yıldız, E., Yıldız, Ö. A. (2026). A Hybrid Framework Based on Transfer Learning for Seasonal Classification of Satellite Images. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 14 Sayı: 3, 1710–1723. https://doi.org/10.29109/gujsc.1958797

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