基于知识工程师的崇明岛东滩自然保护区盐沼植被分类研究  

The Classification of Salt Marsh Vegetation for Chongming Dongtan Nature Reserve Based on Knowledge Engineer

作　　者：阿也提古丽.斯迪克 赵书河[1] 左平[1] 王春红[1] 

机构地区：[1]南京大学地理与海洋科学学院,南京210093

出　　处：《国土资源遥感》2010年第1期123-126,共4页Remote Sensing for Land & Resources

基　　金：国家自然科学基金项目(编号:40501047)资助

摘　　要：以崇明东滩自然保护区盐沼植被为研究对象,利用Landsat TM遥感图像,结合现场调查和前人关于东滩时空动态变化的研究结果,确定崇明岛东滩主要分布的盐沼植被类型,提出了基于知识工程师的植被分类方法。与常规非监督和监督分类相比,该方法的精度较高,总体精度为92.35%,kappa系数为0.9072,而非监督分类和监督分类(最大似然法)的总体精度分别为86.92%和89.10%。实验结果表明,该方法能够有效地对研究区植被进行分类与识别,可为实现盐沼植被的自动提取提供理论依据和有效的方法途径。This paper used Chongming Dongtan Nature Reserve as the research object for salt marsh vegetation classification based on Landsat TM image. According to such image preprocessing measures as image geometric correction and subset image and on the basis of analyses of Landsat TM remotely sensed images integrated with field survey and other studies of spatio - temporal dynamics of Chongming Dongtan Nature Reserve, this paper confirmed the species of the vegetation in this area. The authors used knowledge engineer to classify the vegetation, built knowledge base on the basis of vegetation spectral information and presented a vegetation classification method based on the spectral information. The overall precision of the vegetation classification method based on knowledge engineer is 92.35%, and the kappa coefficient is 0. 907 2. The precision is higher than the overall precision of the vegetation classification based on unsupervised classification and supervised classification （maximum likelihood） ： the overall precisions of unsupervised classification and supervised classification are respectively 86.92% and 90.10%. The result shows that the vegetation classification method can classify and discriminate vegetation effectively and the precision is higher than that of other methods. The vegetation classification method provides a theoretical foundation and effective method for automatic extraction of vegetation.

关 键 词：崇明东滩自然保护区 光谱特征分析 知识工程师 分类实验 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]