一种考虑与主要水体距离关系的海岸带湿地遥感分类方法

A Remote Sensing Classification Methods of Coastal Wetlands Considering the Distance to Main Water

海岸带湿地类型多样、分布复杂,遥感影像中"异物同谱"现象严重,仅仅依靠光谱信息进行地物分类,精度很难保证。针对这个问题,基于湿地在海岸带区域空间分布规律,提出两类距离层,岸线距离层和河流距离层,结合最大似然法和决策树法,发展了一种海岸带湿地遥感信息提取方法,并以江苏省射阳县为例开展了试验应用。研究发现该方法比仅利用光谱信息的最大似然监督分类方法在分类精度上有了较大提高。应用最大似然监督分类方法的总体分类精度为62.30%,总体Kappa系数为0.60,应用本文发展方法的总体分类精度提高到81.50%,总体Kappa系数提高到0.79。

Coastal wetlands is complex,＂different objects with the same spectrum＂ is serious in the remote sensing image,so the classification accuracy only based on spectral information is low. For this issue,based on the coastal zone wetland＇s spatial distribution rule, this paper established two kinds of distance layers, distance to coastline layer and distance to river layer,which applied maximum likelihood method and deci sion tree method, and developed a coastal wetland remote sensing information extraction methods, taking Sheyang County,Jiangsu Province for example. The developed methods highly improved the classification accuracy with the overall classification accuracy of 81.5 %, and Kappa of 0.79. The maximum likelihood su pervised classification method classification accuracy was lower with overall classification accuracy of 62.3%,and Kappa of 0.60.