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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2025 / Cilt: 42 Sayı: 3

Probabilistic Stacked Ensemble and Automated Machine Learning Approach in Remote Sensing Data

Uzaktan Algılama Verilerinde Olasılık Tabanlı Yığılmış Topluluk ve Otomatik Makine Öğrenmesi Yaklaşımı

Selver Güngör(ERCİYES ÜNİVERSİTESİ)0000-0001-9359-5979
Berra Nur Tunç(ERCİYES ÜNİVERSİTESİ)0009-0003-9063-5439
Ümit Haluk Atasever(ERCİYES ÜNİVERSİTESİ)0000-0002-3011-9868
Pages
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Publication Date
2026-09-29
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AI Summary & Key Takeaways

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

Conclusion: The findings show that by combining the strengths of different models, more accurate and more class-balanced predictions can be made compared to single models.

📌 3 Key Takeaways

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Objective & Hypothesis:Background/Aim: In the classification of remote sensing data, Machine Learning (ML) models when used alone fall short in capturing spatial and spectral features.
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Key Findings & Data:Results: The proposed framework achieved the highest Macro-F1 score on both datasets (0.9297 on Indian Pines and 0.9573 on Pavia University), significantly outperforming every individual base learner as well as the tree-based ensembles, the voting strategies and the deep learning architectures included in the comparison.
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Conclusion & Impact:Conclusion: The findings show that by combining the strengths of different models, more accurate and more class-balanced predictions can be made compared to single models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Uzaktan Algılama · Yığılmış Topluluk · Makine Öğrenmesi

Abstract

Background/Aim: In the classification of remote sensing data, Machine Learning (ML) models when used alone fall short in capturing spatial and spectral features. These models, especially in the classification of hyperspectral images with similar spectral characteristics, produce high generalization errors. In this study, a probability-based Stacked Ensemble and Automated Machine Learning (AutoML) approach that carries the confidence information of the base learners into the combination stage is proposed to overcome these issues. Methods: The Stacked model is an ensemble method that combines the outputs of multiple ML or deep learning models with a second meta model. Naive Bayes, Decision Trees, Artificial Neural Networks, K-Nearest Neighbors and Support Vector Machines were used as base learners. The class probability scores produced by these models were presented as input to a meta-learner whose hyperparameters were optimized by AutoML, instead of being used directly as output. Results: The proposed framework achieved the highest Macro-F1 score on both datasets (0.9297 on Indian Pines and 0.9573 on Pavia University), significantly outperforming every individual base learner as well as the tree-based ensembles, the voting strategies and the deep learning architectures included in the comparison. Conclusion: The findings show that by combining the strengths of different models, more accurate and more class-balanced predictions can be made compared to single models.

Özet

Uzaktan algılama verilerinin sınıflandırılmasında makine öğrenmesi modelleri tek başına kullanıldığında mekânsal ve spektral özellikleri yakalamada yetersiz kalmaktadır. Bu modeller, özellikle benzer spektral özelliklere sahip hiperspektral görüntülerin sınıflandırılmasında yüksek genelleme hataları üretmektedir. Bu sorunların üstesinden gelmek için çalışmada, sınıflandırma belirsizliğini en aza indiren olasılık tabanlı Yığılmış Topluluk (Stacked Ensemble) ve Otomatik Makine Öğrenmesi yaklaşımı önerilmiştir. Yığılmış model, birden fazla makine öğrenmesi veya derin öğrenme modelinin çıktılarını ikinci bir meta model ile birleştiren bir topluluk yöntemidir. Çalışmada temel öğreniciler olarak Naive Bayes, Karar Ağaçları, Yapay Sinir Ağları, K-En Yakın Komşu ve Destek Vektör Makineleri kullanılmıştır. Bu modellerin ürettiği sınıf olasılık skorları, doğrudan çıktı olarak kullanılmak yerine, hiperparametreleri Otomatik Makine Öğrenmesi ile optimize edilen bir meta-öğreniciye girdi olarak sunulmuştur. Önerilen model, Indian Pines ve Pavia University veri setlerinde en yüksek Macro-F1 skorunu (sırasıyla 0.9297 ve 0.9573) elde ederek tüm bireysel temel öğrenicileri, ağaç tabanlı toplulukları, oylama yaklaşımlarını ve karşılaştırmaya dâhil edilen derin öğrenme mimarilerini anlamlı biçimde geride bırakmıştır. Elde edilen bulgular, farklı modellerin güçlü yönlerinin birleştirilmesiyle tekil modellere kıyasla daha doğru, daha az yanlı ve daha kararlı tahminler yapılabileceğini göstermiştir.

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Cite This Article

Güngör, S., Tunç, B. N., Atasever, Ü. H., *, B. N. T. (2026). Probabilistic Stacked Ensemble and Automated Machine Learning Approach in Remote Sensing Data. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 3. https://doi.org/10.65520/erciyesfen.1999099

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