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Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.

Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.

Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.

Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.

Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.