首页|综合光谱和纹理信息的遥感影像茶园信息识别

综合光谱和纹理信息的遥感影像茶园信息识别

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
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.
Remote Sensing Image Tea Garden Information Recognition of Comprehensive Spectrum and Texture Information
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.

Zhuhai No.1Sentinel-2tea garden recognitionspectral featurestexture characteristics

韩颖、王泽华、吕杰

展开 >

昆明理工大学 国土资源工程学院,云南 昆明 650093

昆明理工大学 城市学院,云南 昆明 650051

珠海一号 Sentinel-2 茶园识别 光谱特征 纹理特征

国家自然科学基金项目教育部产学合作协同育人项目昆明理工大学课程思政内涵式建设项目2022年度昆明理工大学分析测试基金2022年度昆明理工大学分析测试基金

622660262021010960331096202202162022T201400902022M20212201154

2024

城市勘测
中国城市规划协会 武汉市测绘研究院

城市勘测

影响因子：0.488
ISSN：1672-8262
年,卷(期)：2024.(2)
  • 18