A Hybrid Transfer Learning Framework for Seasonal Classification of Satellite Images

Öz

Seasonal classification from satellite imagery is an important remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns. However, developing robust deep learning models for this task is challenging due to limited labeled data, regional variability, and the computational cost of training large-scale networks from scratch. This study proposes a hybrid transfer learning-based framework for seasonal classification using satellite images collected from 81 provinces of Türkiye. A custom dataset was constructed from monthly satellite images, and eight pretrained deep learning architectures were evaluated as feature extractors. The extracted deep representations were classified using seven machine learning algorithms. The experimental results showed that both the choice of pretrained feature extractor and the classifier affect seasonal classification performance. Among models, ConvNeXt combined with the Multi-Layer Perceptron achieved the best performance. Based on the comparative analysis, ConvNeXt, Vision Transformer, and Swin Transformer were selected as the top three feature extractors, while the Multi-Layer Perceptron was selected as the final classifier. The proposed framework provides an effective and computationally practical approach for seasonal classification and offers a promising basis for future environmental monitoring and agricultural remote sensing applications.

Anahtar Kelimeler

• Transfer learning
• Pretrained models
• Machine learning classification

Uydu Görüntülerinin Mevsimsel Sınıflandırılması için Hibrit Transfer Öğrenme Çerçevesi

Öz

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 etiketli veri, bölgesel değişkenlik ve büyük ölçekli ağların sıfırdan eğitilmesinin hesaplama maliyeti nedeniyle bu görev için sağlam derin öğrenme modelleri geliştirmek zordur. Bu çalışma, Türkiye'nin 81 ilinden toplanan uydu görüntülerini kullanarak mevsimsel sınıflandırma için hibrit bir transfer öğrenme tabanlı çerçeve önermektedir. Aylık uydu görüntülerinden özel bir veri seti oluşturulmuş ve sekiz öneğitilmiş derin öğrenme mimarisi özellik çıkarıcı olarak değerlendirilmiştir. Çıkarılan derin temsiller, yedi makine öğrenme algoritması kullanılarak sınıflandırılmıştır. Deneysel sonuçlar hem öneğitilmiş özellik çıkarıcının hem de sınıflandırıcının seçiminin mevsimsel sınıflandırma performansını etkilediğini göstermiştir. Modeller arasında, çok katmanlı algılayıcı ile birleştirilmiş ConvNeXt en iyi performansı elde etmiştir. Karşılaştırmalı analize dayanarak, ConvNeXt, vision transformer ve swin transformer en iyi üç özellik çıkarıcı olarak seçilirken, çok katmanlı algılayıcı nihai sınıflandırıcı olarak seçilmiştir. Önerilen çerçeve model, mevsimsel sınıflandırma için etkili ve hesaplama açısından pratik bir yaklaşım sunmakta olup, gelecekteki çevresel izleme ve tarımsal uzaktan algılama uygulamaları için umut vadeden bir temel oluşturmaktadır.

Anahtar Kelimeler

• Transfer öğrenme
• Öneğitimli modeler
• Makine öğrenmesi sınıflandırma.

Kaynakça

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