深度学习模型在遥感图像分割中的应用与挑战
Application and challenge of deep learning model in remote sensing image segmentation
王艳群1
作者信息
- 1. 兰州交通大学,甘肃 兰州 730070
- 折叠
摘要
遥感图像分割技术是遥感领域中一项至关重要的任务,对地表覆盖分类、资源监测和环境评估等领域具有显著影响.文章总结了深度学习在遥感图像分割中的应用现状,深入剖析了深度学习在遥感图像分割中所面临的挑战,并提出了一系列深度学习应对挑战的策略,旨在提升遥感图像分割的精度、效率和可解释性.文章对深度学习在遥感图像分割领域的应用前景进行了预测.随着技术的进步,深度学习在遥感图像分割的应用将更加广泛.
Abstract
Remote sensing image segmentation is a crucial task in the field of remote sensing,significantly impacting areas such as land cover classification,resource monitoring,and environmental assessment.This paper summarizes the current state of applications of deep learning in remote sensing image segmentation and provides an in-depth analysis of the challenges faced in this domain.A suite of strategies is proposed to address these challenges,with the aim of enhancing the accuracy,efficiency,and interpretability of remote sensing image segmentation through deep learning.The paper predicts the future prospects of deep learning applications in the field of remote sensing image segmentation.With technological advancements,the application of deep learning in remote sensing image segmentation is expected to expand further.
关键词
遥感图像分割/深度学习/数据增强/模型设计/可解释性Key words
remote sensing image segmentation/deep learning/data augmentation/model design/interpretability引用本文复制引用
出版年
2024
NETL
NSTL
万方数据