北京测绘2024，Vol.38Issue(12)：1724-1727,4.DOI:10.19580/j.cnki.1007-3000.2024.12.013
基于GF-2的海岸带植被提取
Vegetation extraction in coastal zones based on GF-2
孔令尧1
作者信息
- 1. 辽宁省自然资源事务服务中心,辽宁 沈阳 110034
- 折叠
摘要
Abstract
For the existing extraction methods for vegetation in coastal zones,there are problems such as a long investigation period and great difficulty in the investigation,and it is difficult to meet the need of rapidly obtaining the large-scale vegetation status in the coastal zones.To address these issues,this paper studied the coastal area of Dalian City,Liaoning Province and Gaofen-2(GF-2)multispectral images and proposed an extraction method for vegetation in coastal zones based on improved U-shaped convolutional neural networks(Res_UNet).Firstly,the method produced an extraction dataset for vegetation in coastal zones based on sub-meter high-resolution images.Secondly,it fused the residual network(ResNet18)with a network depth of 18 layers and the U-Net network and introduced the residual module into U-Net to improve the fitting accuracy of the model.Finally,performance evaluation experiments were conducted on a test set consisting of 1 200 independent test samples.The results show that the Res_UNet model improves the average intersection over union(IOU)I and F1 by 2.554%and 1.949%compared with the U-Net model.The method proposed in this paper can realize the rapid extraction of large-scale vegetation in coastal zones and provide support for the construction of ecological civilization in coastal zones and the dynamic statistics of natural shoreline retention rate.关键词
高分影像/海岸带植被/自然岸线保有率/深度学习/植被提取Key words
high-resolution image/vegetation in coastal zone/natural shoreline retention rate/deep learning/vegetation extraction分类
天文与地球科学引用本文复制引用
孔令尧..基于GF-2的海岸带植被提取[J].北京测绘,2024,38(12):1724-1727,4.
