综合光谱和纹理信息的遥感影像茶园信息识别
Remote Sensing Image Tea Garden Information Recognition of Comprehensive Spectrum and Texture Information
韩颖 1王泽华 1吕杰2
作者信息
- 1. 昆明理工大学 国土资源工程学院,云南 昆明 650093
- 2. 昆明理工大学 城市学院,云南 昆明 650051
- 折叠
摘要
为了获取准确的茶园空间分布信息,利用珠海一号和 Sentinel-2 号影像数据以最小距离法、支持向量机(SVM)和随机森林(RF)分类方法,结合光谱和纹理特征,对普洱市思茅街道的茶园分布进行识别.结果显示,仅以光谱特征识别,珠海一号和Sentinel-2 的最佳分类效果均为随机森林构建的分类模型:OA分别为 82.98%和 91.29%,Kappa系数分别为0.58 和0.79;将其与纹理特征相结合,珠海一号和Sentinel-2 的最佳分类效果均为随机森林构建的分类模型:OA分别为 95.07%和 95.48%,Kappa系数分别为 0.84 和 0.88.表明在不同的影像数据源中,相比仅以光谱特征识别茶园,将光谱特征和纹理特征二者相结合,可以极大提高茶园的识别精度.
Abstract
In order to obtain accurate information on the spatial distribution of tea plantations,the image data of Zhuhai-1 and Sentinel-2 were utilized to identify the distribution of tea plantations in Simao Street,Pu'er City,using the minimum distance method,Support Vector Machine(SVM)and Random Forest(RF)classification methods,combined with spectral and texture features.The results showed that,with only spectral features,the best classification results of both Zhuhai-1 and Sentinel-2 were the classification models constructed by Random Forest:the OA was 82.98%and 91.29%,and the Kappa coefficients were 0.58 and 0.79,respectively;combining them with texture features,the best classification results of both Zhuhai-1 and Sentinel-2 were the classification models constructed by Random Forest:the OA was 82.98%and 91.29%,and the Kappa coefficients were 0.58 and 0.79,respectively.classification model:the OA is 95.07%,95.48%,and the Kappa coefficient is 0.84 and 0.88,respectively,indicating that the combination of both spectral and texture features can greatly improve the recognition accuracy of the tea plantation compared to recognizing the tea plantation by spectral features only in different image data sources.
关键词
珠海一号/Sentinel-2/茶园识别/光谱特征/纹理特征Key words
Zhuhai No.1/Sentinel-2/tea garden recognition/spectral features/texture characteristics引用本文复制引用
基金项目
国家自然科学基金项目(62266026)
教育部产学合作协同育人项目(202101096033)
昆明理工大学课程思政内涵式建设项目(109620220216)
2022年度昆明理工大学分析测试基金(2022T20140090)
2022年度昆明理工大学分析测试基金(2022M20212201154)
出版年
2024
NETL
NSTL
万方数据