一个具有空间位置集成的卷积神经网络,用于近岸水深逆转
Chunlong He1, Qigang Jiang1, Guofang Tao1
1College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
一个新的卷积神经网络与空间位置集成 (CNN-SLI) 通过从遥感数据中提取更深的特征来改善近岸水深逆转. 这种方法为沿海研究和管理提供了卓越的准确性和概括性.
科学领域:
- 遥感和地理空间分析
- 沿海工程与管理
- 机器学习和深度学习应用程序
背景情况:
- 准确的近岸水深对于航行,海岸保护和减灾至关重要.
- 现有的遥感方法因缺乏足够的特征提取而难以实现精确的近岸水深逆转.
- 整合空间信息可以提高深度学习模型在地理空间任务中的性能.
研究的目的:
- 利用遥感数据开发一种改进的近岸水深逆转方法.
- 为了解决当前近岸水域测量技术中特征提取的局限性.
- 评估一种包含空间位置信息的新型卷积神经网络的有效性.
主要方法:
- 提出了一个卷积神经网络与空间位置集成 (CNN-SLI).
- 集成的像素空间位置作为额外的道进入CNN的输入数据.
- 利用GF-6遥感图像和电子海图数据进行南山港附近的实验.
主要成果:
- CNN-SLI实现了优越的近岸水深逆转精度,RMSE为1.34米,MAE为0.94米,R2为0.97.
- 该模型在浅水和深水中表现一致,优于Lyzenga,MLP和CNN模型.
- 与传统和其他深度学习模型相比,CNN-SLI在独立数据集上显示出更好的概括能力.
结论:
- 通过有效利用空间信息,CNN-SLI模型显著提高了近岸水深逆转精度.
- 将空间位置数据集成到深度学习架构中对于改进基于遥感的浴度测量至关重要.
- 拟议的CNN-SLI方法为沿海地区管理和科学研究提供了强大而准确的解决方案.
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