文献类型：期刊文献

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

作者：Lv, Jinna[1];Shen, Qi[2];Lv, Mingzheng[3];Li, Yiran[1];Shi, Lei[4];Zhang, Peiying[5]

第一作者：Lv, Jinna

通讯作者：Shen, Q[1]

机构：[1]Beijing Informat Sci & Technol Univ, Sch Informat Management, Beijing, Peoples R China;[2]Beijing Union Univ, Teachers Coll, Beijing, Peoples R China;[3]Shangqiu Normal Univ, Sch Int Educ, Shangqiu, Peoples R China;[4]Commun Univ China, State Key Lab Media Convergence & Commun, Beijing, Peoples R China;[5]China Univ Petr East China, Coll Comp Sci & Technol, Qingdao, Peoples R China

第一机构：Beijing Informat Sci & Technol Univ, Sch Informat Management, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Teachers Coll, Beijing, Peoples R China.|[1141711]北京联合大学师范学院;[11417]北京联合大学;

年份：2023

卷号：11

外文期刊名：FRONTIERS IN ECOLOGY AND EVOLUTION

收录：;Scopus(收录号：2-s2.0-85175111018);WOS:【SCI-EXPANDED(收录号:WOS:001038201300001)】；

基金：This work was partially supported by the R & amp;D Program of Beijing Municipal Education Commission (No. KM202211417014), the Academic Research Projects of Beijing Union University (No. ZK20202215), the Natural Science Foundation of Shandong Province under Grant ZR2022LZH015 (ZR2020MF006), the Industry-University Research Innovation Foundation of Ministry of Education of China under Grant (2021FNA01001), and the Shandong Provincial Natural Science Foundation, China under Grant ZR2020MF006 and ZR2022LZH015.

语种：英文

外文关键词：remote sensing; deep learning; convolutional neural network; semantic segmentation; satellite image

摘要：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.