Abstract
Remote sensing technologies have revolutionized the way we observe and analyze Earth’s surface from afar. With the increasing availability of high-resolution satellite and aerial imagery, there is a growing need for efficient and accurate methods to extract valuable information from these images. Machine learning (ML) techniques have emerged as powerful tools for remote sensing image analysis, enabling automated and data-driven solutions for a wide range of applications. This review paper provides a comprehensive overview of the state-of-the-art machine learning approaches applied to remote sensing image analysis. We categorize these approaches based on image pre-processing, feature extraction, classification, segmentation, and object detection. Additionally, we discuss challenges, trends, and potential future directions in the field, highlighting the potential impact of ML on advancing our understanding of the Earth’s dynamic processes.
Authors
