基于面向对象CNN和RF的不同空间分辨率遥感影像农业大棚提取研究
Different Spatial Resolutions based on Object-oriented CNN and RF Research on Agricultural Greenhouse Extraction from Remote Sensing Images
林欣怡 1汪小钦 1汤紫霞 1李蒙蒙 1吴瑞姣 2黄德华2
作者信息
- 1. 福州大学 空间数据挖掘与信息共享教育部重点实验室,福建 福州 350108;福州大学 数字中国研究院(福建),福建 福州 350108
- 2. 福建省地质测绘院,福建 福州 350011
- 折叠
摘要
遥感技术已成为快速有效获取农业大棚覆盖信息的重要途径,但遥感影像空间分辨率大小对提取精度的影响具有双重性,选择适宜分辨率影像具有重要意义.以南方农业塑料大棚为研究对象,利用GF-1、GF-2和Sentinel-2形成1～16 m间6个不同空间分辨率影像数据集,基于面向对象影像分析方法(Object-Based Image Analysis,OBIA),分别利用面向对象卷积神经网络(Convolutional Neural Network,CNN)方法和随机森林(Random forest,RF)方法开展大棚提取,分析提取精度和不同方法下的差异性.结果表明:①CNN和RF方法下,农业大棚的提取精度随着影像分辨率降低总体呈下降趋势,在1～16 m的影像上均能检测到农业大棚;②相对于RF方法,CNN方法对影像空间分辨率要求更高,在1～2 m分辨率下,CNN方法有更少的漏提和误提,但在4m及更低分辨率下,RF方法的适用性更高;③2 m分辨率影像是大棚信息提取的最佳空间分辨率,可经济有效地实现大棚监测.
Abstract
Remote sensing technology has become an important way to obtain agricultural greenhouse coverage information quickly and effectively.But the spatial resolution size of remote sensing images has a dual influence on the extraction accuracy,and it is important to select suitable resolution images.Taking the southern agricul-tural plastic greenhouses as the research object,GF-1,GF-2 and Sentinel-2 are used to form six different spa-tial resolution image datasets between 1 and 16 m.Based on Object-Based Image Analysis(OBIA),we use the Convolutional Neural Network(CNN)and Random Forest(RF)methods to extract the canopy and ana-lyze the extraction accuracy and the difference between the methods.The results show that:(1)the extraction accuracy of agricultural greenhouses under CNN and RF methods generally decreases as the image resolution de-creases,and agricultural sheds can be detected on images from 1m to 16 m;(2)the CNN method requires high-er spatial resolution than the RF method,and the CNN method has fewer missed and false extractions at 1～2 m resolution,but at 4 m and lower resolutions,the RF method is more applicable;(3)the 2 m resolution im-age is the best spatial resolution for shed information extraction,which can realize shed monitoring economically and effectively.
关键词
农业大棚提取/面向对象CNN方法/随机森林/空间分辨率/高分辨率遥感数据Key words
Agricultural greenhouse extraction/Object-oriented CNN method/Random forest/Spatial resolu-tion/High-resolution remote sensing image引用本文复制引用
基金项目
福建省科技创新基金(2022C0024)
中央引导地方科技发展专项(2017L3012)
出版年
2024
NETL
NSTL
万方数据