摘要
针对省域等大尺度景观格局研究问题,以广东省为研究对象,采用10 m分辨率的Sentinel-2遥感影像及其对应的土地利用类型数据,以景观格局指数、遥感影像为特征集,以基于视觉特征的影像类型为标签集,采用Adam优化算法构建基于Keras深度学习框架的景观视觉特征分类模型。将特征集与对应的标签集数据按8∶2分为训练集和测试集,进行交叉验证,结果表明:模型在训练集和测试集上的准确度分别达99.57%、98.93%。模型能有效关联景观格局指数与影像视觉特征,泛化能力强,适用于大区域景观格局研究及乡镇布局规划中的遥感影像分类任务。
Aiming at the research problems of large-scale landscape patterns such as provinces,taking Guangdong Province as the research object,using Sentinel-2 remote sensing images with a resolution of 10m and their corresponding land use type data,taking landscape pattern index and remote sensing images as feature sets,and taking image types based on visual features as label sets,Adam optimization algorithm is used to construct a landscape visual feature classification model based on Keras deep learning framework.The feature set and the corresponding label set data are divided into training set and test set according to 8∶2,and cross-validated.The results show that the accuracy of the model on the training set and test set is 99.57%and 98.93%,respectively.The model can effectively correlate landscape pattern index and image visual features,and has strong generalization ability.It is suitable for remote sensing image classification tasks in large-scale regional landscape pattern research and township layout planning.
作者
马彦彤
罗勇
MA Yantong;LUO Yong(School of Geography&Environmental Economics,Guangdong University of Finance&Economics,Guangzhou 510320,China;Guangzhou Institute of Geochemistry,Chinese Academy of Sciences,Guangzhou 510640,China)
出处
《航天返回与遥感》
北大核心
2025年第1期109-122,共14页
Spacecraft Recovery & Remote Sensing
基金
广东省矿物物理与材料研究开发重点实验室开放基金(20217B030314175)
广州市科技计划项目(201804010294)。
关键词
景观格局指数
深度学习
神经网络
遥感影像分类
landscape pattern index
deep learning
neural network
remote sensing image classification
