Abstract
Since the advent of the IKONOS satellite in 1999, remote sensing has witnessed remarkable enhancements in spatial resolution, propelled further by satellites such as QuickBird, the WorldView series, GeoEye-1, Pleiades, and others, including UAVs. This technological advancement has facilitated the development of high-resolution images that significantly differ from moderate- or low-resolution ones by enabling the identification of distinct land cover features, or “objects,” crucial for Geographic Object-Based Image Analysis (GEOBIA). Central to GEOBIA, image segmentation partitions images into coherent regions representing real-world elements like buildings and vegetation, which is crucial for accurate classification. This chapter reviews segmentation algorithms, categorizing them into traditional and deep learning (DL)-based semantic segmentation. Traditional methods group pixels into objects without class assignment, requiring additional classification steps, whereas DL-based semantic segmentation combines segmentation and classification, enhancing precision. The chapter traces the evolution of these methods, noting a significant increase in traditional segmentation with high-resolution imagery and an exponential rise in DL-based approaches. It explores state-of-the-art DL-based algorithms, their concepts, advantages, limitations, and applications. Additionally, it highlights the application of segmentation in various imaging contexts, including multispectral, hyperspectral, and LiDAR images, offering a comprehensive analysis of the current trends and future directions in remote sensing image segmentation.