Hybrid Image Classification Model using ResNet101 and VGG16

Deep convolution neural networks have made sig-nificant advances in object identification. The popularity of machine learning-based image classification has increased as a result of developments in deep learning algorithms that makes it possible to extract features from images. Yet, conventional image classification algorithms are far too incorrect and untrustworthy to address the problem. Automation is crucial due to the vast geographic areas that must be explored and the scarcity of researchers available to carry out the searches. The proposed work employs deep learning-based image classification using a hybrid model of ResNet101 and VGG16 to address the challenges of image classification in large geographic areas using satellite images.