Image Captioning of Satellite Images Using Transfer Learning and LSTM Blending

In recent years, the field of image captioning has gained substantial attention, posing a complex challenge that necessitates the integration of computer vision (CV), natural language processing (NLP), and machine learning techniques. Our study introduces an advanced model designed to generate natural language descriptions for satellite images. We harnessed the capabilities of convolutional neural networks (CNNs) and utilized state-of-the-art architectures such as VGG-19, ResNet-50, DenseNet-201, and EfficientNet B7 as encoders to extract intricate features from satellite images. These encoded features were seamlessly incorporated into a Long Short-Term Memory (LSTM) network, which is a type of recurrent neural network (RNN), serving as a robust decoder. To assess the effectiveness of our approach, we conducted extensive experiments using two datasets: the Sydney Dataset and the RSIC Dataset, both widely recognized benchmarks in the field of image captioning. The comparative analysis results obtained from our evaluations not only show promise but also underscore the competitiveness of our model. By achieving significant milestones in satellite image captioning, this research not only contributes to the expanding body of knowledge in this domain but also opens avenues for real-world applications, spanning from environmental monitoring to urban planning.