CONVCAT: A NEW CLASSIFICATION APPROACH USING UC MERCED AND RESISC45 DATASETS

Abstract

With advances in Earth observation systems, the importance of remote sensing data is increasing daily. These data are used in various fields ranging from image segmentation to terrain classification, from disaster impact assessment to climate change analysis. The use of remotely sensed images for terrain classification has been the subject of a number of studies. This study proposes a new method for terrain classification in the UC Merced Land Use Dataset and RESISC45 remote sensing images. This method is called ConvCat model, which is a combination of classical convolutional layer and CatBoost models. The performance of this model is measured in terms of accuracy, the Matthews Correlation Coefficient (MCC) and the Cohen's Kappa metrics. The results are compared with ensemble models (XGBoost, CatBoost), with ConvXGB, a combination of convolutional learning and XGBoost, and with ResNet50, one of the most widely used transfer learning models. The developed ConvCat model outperformed the other models, achieving an accuracy of 97.44% on the UC Merced data set and an accuracy of 96.89% on the Resisc45 data set. This study shows that our newly developed model provides the best results for the classification problem based on remote sensing images.

Keywords

• Remote Sensing
• Transfer Learning
• Ensemble Learning
• ConvCat
• Ground Observation Classification

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