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，将原有各波段图像的显著且独立的信息集中在数目尽可能少的合成图像中；再依据不同类型水体的光谱特性，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，从而根据具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，这一方法采用主成分分析确定标志类型，无需大量的现场调查，因而具有非监督聚类成本低的优点，分类结果则优于非监督法，且各类型的生态意义明显，分布特征与环境因子相互吻合，是水域水质环境图像识别的有效而实用的方法。
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 largearea 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.
出处
《湖泊科学》
EI
CAS
CSCD
1997年第3期261-268,共8页
Journal of Lake Sciences
基金
国家自然科学基金!39500027
江苏省社会发展研究基金!BS95035
关键词
遥感识别
水质特征
太湖
化学成分
水质环境
Principal component analysis, image recognition, water quality, Taihu Lake
