Deep learning-based semantic segmentation of remote sensing images: a review

Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

Automated summary

Semantic segmentation is a challenging task in pixel-level remote sensing data analysis. Deep learning methods have been successfully applied and improved in this field, leading to excellent results. However, there is still a deficiency in the evaluation and advancement of semantic segmentation techniques for remote sensing data. This paper surveys more than 100 papers in the past 5 years and comprehensively summarizes the advantages and disadvantages of techniques and models based on important and difficult points, providing valuable insights for beginners in this field.