基于ALOS影像的盐城海滨湿地遥感信息分类方法研究

盐城海滨湿地类型丰富多样,湿地植物覆被类型之间的生态交错带十分明显,如何更为准确地获得海滨湿地覆盖信息,对湿地研究具有重要价值和意义。以ALOS影像为数据源,江苏盐城海滨湿地核心区为试验区,开展湿地信息遥感分类研究。在对研究区进行非监督分类,分析其限制分类精度原因基础上,针对研究区域的特点提出适合的分类精度改进方法。以非监督分类后的结果为模板,借助分区分层分类方法的思想,通过分析遥感影像光谱信息、纹理信息、主成分变换信息,得到知识规则,以基于知识规则修改的方法对芦苇、米草和盐蒿3种植被交错带进行修正。然后以基于GIS规则的方法对剩余区域进行修正。通过GPS数据进行精度检验,分类精度达到92.6829%,Kappa系数为0.9098。实验证明基于GIS规则和知识规则的分区分层分类法是提高海滨湿地遥感分类精度的有效方法。

Study on the Classification Approaches of Yancheng Coastal Wetlands based on ALOS Image

The Yancheng coastal wetlands are rich in biodiversity and of significant importance in wetland conservation of the world.How to acquire coastal wetland types information more accurately using remote sensing images is significant to wetland researches.The core zone of Yancheng coastal wetlands are taken as a study area and the classification of wetland types is based on ALOS image.We developed the following methods to improve the classification accurately of wetland types.At first,we use the unsupervised classification method to conduct the primary classification system for the coastal wetlands.Then,the reason for limiting the accuracy of wetland types were found,especially in the ecotones of wetland types.The ecotones among reed marsh,spartina alterniflora marsh and suaeda heteroptera kitag marsh have been revised by using the spectral feature of wetlands,texture,principal component analysis and relative knowledge rules.In addition,the rest parts were revised by GIS rules.Finally,the precision of classification was tested by the GPS data and the average accuracy of wetland types has reached 92.6829% the kappa coefficient reaches 0.9098 in the test region.Which suggested that the multi-level classification method including the knowledge rules and GIS rules are effective to extracting the coastal wetland cover information.