DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-2 ( Feb, 2024 ) | |||||||||
Paper Title |
An Ensemble CNN Model for Land Cover Classification | |||||||||
Author Name |
Chayanika Basak, Kanushree Anand, Pratibha, Shailesh D. Kamble | |||||||||
Affilition |
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Pages |
54-62 | |||||||||
Abstract |
One of the interesting uses of Earth observation satellite data is „Land Cover Classification‟. In this paper, data containing geospatial images from the Sentinel-2 satellite have been utilized to categorize land cover as „Forest‟, „Annual Crop‟, „Highway‟, „Herbaceous Vegetation‟, „Industrial‟, „Pasture‟, „Residential‟, „River‟, „Permanent Crop‟ and „Sea Lake‟. After pre-processing the dataset of satellite images, it was passed through 3 pre-trained Convolutional Neural Network models, namely ResNet18, VGG16 and DenseNet121 in order to classify these images into the above-mentioned 10 classes. In order to further improve accuracy and make more accurate classifications, an ensemble model was created using the 3 CNN models and trained on the same data. It was observed that the ensemble model actually generated accuracy (96.852%) higher than the highest performing CNN model which in this case was the Resnet18 with 95.208% accuracy. Keywords - ResNet, DenseNet, VGG, Convolutional Neural Network, Land Cover Classification, Satellite Images, Ensemble | |||||||||
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