土地使用和土地覆盖分类与深度学习相结合:一篇综述
Shengyu Zhao1, Kaiwen Tu1, Shutong Ye1
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
深度学习在地球观测中显著推进了土地利用和土地覆盖 (LULC) 的分类. 本综述涵盖了深度学习网络,数据集,策略,挑战和LULC图像分析的未来趋势.
科学领域:
- 地球观测 地球观测
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 土地使用和土地覆盖 (LULC) 分类对于环境监测和资源管理至关重要.
- 高维的遥感数据和有限的标记样本对传统的LULC分类构成重大挑战.
- 深度学习已经成为提高LULC分类准确性的强大工具.
研究的目的:
- 为LULC分类提供基于深度学习的方法进行系统审查.
- 涵盖深度学习网络,数据集,评估指标和策略的关键组成部分.
- 讨论该领域的挑战和未来方向.
主要方法:
- 概述了五种典型的深度学习网络架构及其好处.
- 对于LULC分类的基线数据集和性能指标 (OA,AA,F1,MIOU) 的总结.
- 探索深度学习策略,包括CNN,AE,GAN和RNN.
主要成果:
- 深度学习方法在LULC分类中取得了显著的成果.
- 识别用于LULC分类的常见深度学习策略.
- 分析与有限的训练样本在遥感图像分类相关的挑战.
结论:
- 深度学习为LULC分类研究和开发提供了新的可能性.
- 应对有限的培训数据等挑战是未来进步的关键.
- 未来的发展可能会专注于更复杂的深度学习模型和技术.
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