基于Sentinel-2A影像的滇中高原蜻蛉河灌区农作物种植结构提取研究  

Study on Crop Planting Structure Extraction of Qingling River Irrigation Area in the Central Yunnan Plateau Based on Sentinel-2A Images

作　　者：钟雪 杨明龙[1,2] 唐秀娟 韩澳禧 ZHONG Xue;YANG Minglong;TANG Xiujuan;HAN Aoxi(School of Land Resources Engineering Kunming University of Science and Technology;Application Engineering Research Cen-ter of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Prov-ince,Kunming 650093,China;Kunming Surveying and Mapping Institute,Kunming 650091,China)

机构地区：[1]昆明理工大学国土资源工程学院 [2]云南省高校高原山区空间信息测绘技术应用工程研究中心,云南昆明650093 [3]昆明市测绘研究院,云南昆明650091

出　　处：《软件导刊》2024年第12期198-205,共8页Software Guide

基　　金：国家自然科学基金项目(41861054,62266026)。

摘　　要：滇中高原地区海拔高,农作物分布破碎,种植地块面积小,快速准确地获取农作物种植结构对于当地农业灌溉、估产等意义重大。目前鲜有基于Sentinel-2A影像数据对滇中高原农作物复杂地区的研究,为此,基于光谱、纹理、地形3类特征构建神经网络、支持向量机与随机森林分类器,分析比较得到适合灌区的特征组合与最佳分类器。实验结果表明:3种分类模型中,支持向量机更适合灌区的种植结构提取,其总体精度为91.74%,Kappa系数为0.87。在此基础上构建面向对象的支持向量机模型,农作物提取整体精度得到进一步提升,总体精度为93.87%,Kappa系数为0.90,与传统的3种特征组合的支持向量机法相比总体精度提高了2.13%。面向对象的支持向量机法适用于滇中高原蜻蛉河大型灌区的农作物分类,可为当地水利灌溉和农业经济发展提供帮助。The high altitude of the Central Yunnan Plateau region results in fragmented crop distribution and small planting areas.Obtaining the crop planting structure quickly and accurately is of great significance for local agricultural irrigation and yield estimation.At present,there is little research on the complex crop areas in the central Yunnan Plateau based on Sentinel-2A image data.Therefore,a neural network,support vector machine,and random forest classifier are constructed based on the combination of spectral,texture,and terrain features.The suitable feature combination and optimal classifier for irrigation areas are analyzed and compared.The experimental results show that among the three classification models,support vector machines are more suitable for extracting planting structures in irrigation areas,with an overall accuracy of 91.74%and a Kappa coefficient of 0.87.On this basis,an object-oriented support vector machine model was constructed,and the overall accuracy of crop extraction was further improved,with an overall accuracy of 93.87%and a Kappa coefficient of 0.90.Compared with the traditional three feature combination support vector machine method,the overall accuracy was improved by 2.13%.The object-oriented support vector machine method is suitable for crop classification in the large-scale irrigation area of Qingling River in the central Yunnan Plateau,and can provide assistance for local water conservancy irrigation and agricultural economic development.

关 键 词：Sentinel-2A 种植结构 特征提取 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]