Image Captioning Using Deep Convolutional Neural Networks (CNNs)

Abstract

Abstract Earth is challenging to label satellite image clips with atmospheric conditions and various classes of land cover and land use. We proposed an algorithms to help the global community for a better understanding that where, how, and why deforestation take place all over the world. Upcoming development in satellite imaging technology have set grow to new opportunities for more precise investigation of both broad and minute changes occurring on Earth, including deforestation. Since 40 years, almost a fifth of the Amazon rain forest has been cut down. To estimate and analysis the forest this application is developed. Satellite images are trained on deep convolutional neural networks (CNNs) to learn image features and used multiple classification frameworks including gate recurrent unit label captioning and sparse_cross_entropy to predict multiclass, multi-label images. By fine-tuning an architecture consisting of the encoder of pre-trained VGG-19 parameters trained on ImageNet data together with the GRU decoder.