Check for Semantic Segmentation of Remote Sensing Images: Definition, Methods, Datasets and Applications

Abstract

Semantic segmentation of remote sensing images is a vital task in the field of remote sensing and computer vision. The goal is to produce a dense pixel-wise segmentation map of an image, where a specific class is assigned to each pixel, enabling detailed analysis and understanding of the Earth's surface. This paper provides an overview of semantic segmentation in remote sensing, starting with a definition of the task and its significance in extracting valuable information from remote sensing imagery. Various methods used for semantic segmentation in remote sensing are discussed, including traditional approaches such as region-based and pixel-based methods, as well as more recent deep learning-based techniques. Next, the paper delves into the available datasets for semantic segmentation of remote sensing images. Many available datasets are reviewed, highlighting their characteristics, including the number of images, image size, number of labels, spatial resolution, format and spectral bands. These datasets serve as valuable resources for training, evaluating, and benchmarking semantic segmentation algorithms in remote sensing applications. Furthermore, the paper highlights the broad range of applications enabled by semantic segmentation in remote sensing, including urban planning, land cover mapping, disaster management, environmental monitoring, and precision agriculture. Overall, this paper serves as a comprehensive guide to semantic segmentation of remote sensing images, providing insights into its definition, methods, available datasets and wide-ranging applications.