A review on remote sensing imagery augmentation using deep learning

Abstract

The quantity of remote sensing pictures being captured is rising dramatically due to advancements in earth observation technology, bringing remote sensing in a long and distinct period of history of big data. Discovering how to successfully harvest this enormous intensity of remote sensing data will be a new challenge. With deep learning, remote sensing data analysis may take on a narrative tone. CNNs are a Deep Learning model capable of directly extracting features from large volumes of picture data and are effective at exploiting realistic imagery data characteristics. With regards to computer vision, CNNs have shown to be highly successful. CNNs are a powerful tool. There have been several studies employing CNNs for remote sensing picture classification in the last few years, and these studies show that CNNs can produce fast and accurate results. It is the goal of this study to offer an overview of the most current and advanced applications of deep learning based on CNNs for remote sensing picture enhancement. To begin, the concepts and features of convolutional neural networks (CNNs) will be briefly reviewed in this review. There are new innovations and structural enhancements to neural network models that make them more suited for RS picture enhancement, as well as data for remote sensing image categorization and augmentation approaches that may be obtained easily and quickly.. As a result of the survey, we expect remote sensing scientists will be better equipped to tackle categorization challenges utilizing cutting-edge deep learning algorithms and methodologies. To increase the number of training examples with a considerable reduction in model over-fitting, data augmentation is a well-known approach used in most ML & AI applications.