| 主成分监督分类及其在水质特征遥感图像识别中的应用 |
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| 引用本文： | 佘丰宁,蔡启铭.主成分监督分类及其在水质特征遥感图像识别中的应用[J].湖泊科学,1997,9(3):261-268. |
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| 作者姓名： | 佘丰宁 蔡启铭 |
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| 作者单位： | 中国科学院南京地理与湖泊研究所, 南京 210008,中国科学院南京地理与湖泊研究所, 南京 210008 |
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| 基金项目： | 国家自然科学基金(39500027)和江苏省社会发展研究基金(BS95035)资助项目 |
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| 摘 要： | 建立了一种水域水质状况图像识别的主成分监督分类方法，首先通过ＴＭ水域图像数据的主成分分析，将原有各波段图谱的显著且独立的信息集中在数目尽可能少的合成图象中，再依据不同类型水体的光谱特征，分析各主成分图像的构成及其环境生态学含义，由此对整个研究区域内存在的不同标志类型及其分布特征有所了解，在此基础上，选定训练样本集，从而人有清楚的环境生态意义的标志类型，应用监督法得到较好的识别分类结果，分析表明，这
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| 关 键 词： | 遥感识别 水质特征 太湖 化学成分 水质环境 |
| 收稿时间： | 1996/1/16 0:00:00 |
| 修稿时间： | 4/3/1996 12:00:00 AM |
Principal-component-supervised classification and its application to image recognition of water quality |
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| She Fengning and Cai Qiming.Principal-component-supervised classification and its application to image recognition of water quality[J].Journal of Lake Science,1997,9(3):261-268. |
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| Authors: | She Fengning and Cai Qiming |
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| Institution: | Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, Nanjing 210008 and Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, Nanjing 210008 |
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| Abstract: | A new classification method of remote sensing image recognition, called Principal Component-Supervised Classification, is presented. Firstly, by means of principal component analysis, the component images are uncorrelated with each other and explain progressively less of the variance found in the original Landsat Thematic Mapper (TM) data in water area. After analyzing the composition of each component image and its eco-environmental implication according to spectrum features of different water types, the existing water types and their distribution features in the water area are known. Then, the training samples are selected based on the sample water types in PCA images and the classification image is produced following one of the decision rules and programs of supervised methods. This PC-Supervised method, selecting training samples based on the result image of PCA without large-area investigation on the ground or water surface, has thd advantages of unsupervised classification and a partition resolution higher than that of cluster analysis, Furthermore, its distinguishing result, applied to water quality recognition in the northern part of Taihu Lake, shows that the presented water types and their distributions are concordant with the conditions of lake body and environmental factors. So, it is indicated that PC-Supervised classification is an effective and practical method for dynamic analysis of water quality using remote sensing information. |
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| Keywords: | Principal component analysis image recognition water quality Taihu Lake |
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