Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis
PDFAbstract
Training the neural network using high-quality labeled data is a major challenge, as there are many grey areas in image classification. Hence, experts are available to label the data. Proposed in this research paper is a Heterogeneous Multi-Stream Deep Learning Framework. Its application of six advanced CNNs will remove such complications using their complementary inductive biases. An evaluation of two fusion paradigms was successfully achieved: a decision-level weighted average ensemble and a multi-stream CNN-SVM at both feature levels. The feature-level fusion method was found to be more discriminative than probabilistic averaging in testing across three different datasets: NWPU-RESISC45, UC Merced, and AID. This approach achieves the best results on all datasets, with our method achieving a highest F1-score of 97.24% on the NWPU-RESISC45 benchmark. The performance of the six-stream design, having 8192 features, was slightly affected, dropping to 97.15% because of the curse of dimensionality. The findings support the five-stream CNN-SVM as the best architecture since it easily strikes a balance between feature richness and the complexity of the classifier.
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Dhanesha, Jayesh, and Sweta Panchal. "Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis." Journal of Innovative Image Processing 8, no. 1 (2026): 54-71. doi: 10.36548/jiip.2026.1.004
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Dhanesha, J., & Panchal, S. (2026). Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis. Journal of Innovative Image Processing, 8(1), 54-71. https://doi.org/10.36548/jiip.2026.1.004
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Dhanesha, Jayesh, et al. "Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis." Journal of Innovative Image Processing, vol. 8, no. 1, 2026, pp. 54-71. DOI: 10.36548/jiip.2026.1.004.
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Dhanesha J, Panchal S. Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis. Journal of Innovative Image Processing. 2026;8(1):54-71. doi: 10.36548/jiip.2026.1.004
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J. Dhanesha, and S. Panchal, "Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis," Journal of Innovative Image Processing, vol. 8, no. 1, pp. 54-71, Mar. 2026, doi: 10.36548/jiip.2026.1.004.
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Dhanesha, J. and Panchal, S. (2026) 'Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis', Journal of Innovative Image Processing, vol. 8, no. 1, pp. 54-71. Available at: https://doi.org/10.36548/jiip.2026.1.004.
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@article{dhanesha2026,
author = {Jayesh Dhanesha and Sweta Panchal},
title = {{Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis}},
journal = {Journal of Innovative Image Processing},
volume = {8},
number = {1},
pages = {54-71},
year = {2026},
publisher = {Inventive Research Organization},
doi = {10.36548/jiip.2026.1.004},
url = {https://doi.org/10.36548/jiip.2026.1.004}
}
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Keywords
Aerial Scene Classification
Deep Learning
Feature Fusion
CNN-SVM
Ensemble Learning
NWPU-RESISC45
UC Merced
AID
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