Paper Title :An Ensemble CNN Model for Land Cover Classification

Author :Chayanika Basak, Kanushree Anand, Pratibha, Shailesh D. Kamble

Article Citation :Chayanika Basak ,Kanushree Anand ,Pratibha ,Shailesh D. Kamble , (2024 ) " An Ensemble CNN Model for Land Cover Classification " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 54-62, Volume-12,Issue-2

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

Type : Research paper

Published : Volume-12,Issue-2

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