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<p>Satellite image classification has many important applications that play a crucial role in the areas of urban planning, agriculture, as well as environmental monitoring. Nevertheless, the high accuracy and interpretability of deep learning models with such complex datasets is still a big challenge. To solve this, a new hybrid deep-learning architecture, MRCL-ELM is proposed to improve the performance of satellite image classification. The model uses EfficientNetB0 as its building block in terms of ability to learn rich features in an efficient manner with optimization of the network depth and size to minimize memory and processing requirements. It combines the Multi-Residual Convolutional (MRC) networks to learn spatial features robustly with the aid of multiple residual paths, in each block, in MRC, to enhance the learning of features and gradient flow. It uses a Long Short-Term Memory (LSTM) time series modeling layer, and an Extreme Learning Machine (ELM) to quickly and non-iteratively classify data and is therefore lightweight, accurate, and more scalable than other common deep learning architectures. To enhance the interpretability of the proposed model, Local Interpretable Model-agnostic Explanations (LIME) explains individual predictions by testing small variations in the input, whereas SHapley Additive exPlanations (SHAP) provides feature importance scores throughout the model, along with improving model interpretability and trust. The proposed model provides the highest possible results, with 98.33% accuracy on the EuroSAT dataset and 98.10% accuracy on the UC Merced Land Use dataset, being higher than the use of existing Convolutional Neural Networks (CNN) and transformer-based techniques. The training using fixed random seeds and 5-fold cross-validation is used to ensure robustness. Lastly, MRCL-ELM was implemented as a real-time web-based application and tested with real-life Google Maps imagery, and thus needs real-time, precise, and interpretable satellite image classification for the end-users.</p>

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Enhancing multi-class satellite image classification with MRCL-ELM: a hybrid explainable deep learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Md Ashik Ahmmed,
  • Rashel Mahmud Rabbi,
  • Md Shafiuzzaman,
  • Md. Faysal Ahamed,
  • Md Nahiduzzaman,
  • Muhammad E.H. Chowdhury

摘要

Satellite image classification has many important applications that play a crucial role in the areas of urban planning, agriculture, as well as environmental monitoring. Nevertheless, the high accuracy and interpretability of deep learning models with such complex datasets is still a big challenge. To solve this, a new hybrid deep-learning architecture, MRCL-ELM is proposed to improve the performance of satellite image classification. The model uses EfficientNetB0 as its building block in terms of ability to learn rich features in an efficient manner with optimization of the network depth and size to minimize memory and processing requirements. It combines the Multi-Residual Convolutional (MRC) networks to learn spatial features robustly with the aid of multiple residual paths, in each block, in MRC, to enhance the learning of features and gradient flow. It uses a Long Short-Term Memory (LSTM) time series modeling layer, and an Extreme Learning Machine (ELM) to quickly and non-iteratively classify data and is therefore lightweight, accurate, and more scalable than other common deep learning architectures. To enhance the interpretability of the proposed model, Local Interpretable Model-agnostic Explanations (LIME) explains individual predictions by testing small variations in the input, whereas SHapley Additive exPlanations (SHAP) provides feature importance scores throughout the model, along with improving model interpretability and trust. The proposed model provides the highest possible results, with 98.33% accuracy on the EuroSAT dataset and 98.10% accuracy on the UC Merced Land Use dataset, being higher than the use of existing Convolutional Neural Networks (CNN) and transformer-based techniques. The training using fixed random seeds and 5-fold cross-validation is used to ensure robustness. Lastly, MRCL-ELM was implemented as a real-time web-based application and tested with real-life Google Maps imagery, and thus needs real-time, precise, and interpretable satellite image classification for the end-users.