辅以波谱分析的高分辨率影像面向对象分类研究

随着遥感影像空间分辨率的提高，地物的空间信息更加丰富，地物尺寸、 形状以及相邻地物的关系得到更好的反映，因此目前高分辨率影像分类方法更侧重于利用地 物的空间信息，分类过程中参与较多的人为主观因素，在地物类型未知的地区很难进行解译 工作。另外，分割过于细碎导致操作数据量太大也是高分辨率影像分类的难题之一。论文提 出了辅以波普分析的高分辨率影像面向对象分类方法，即在传统面向对象分类方法的基础上 结合影像波谱分析，先对影像光谱角制图粗分类、掩膜操作，再面向对象精分类，较好解决 了以往面向对象分类方法地物类型的不确定性和分割细碎等问题。试验以空间分辨率为0.5 米的八波段WorldView2 影像为研究数据提取西部那曲地区道路和河流，精度达到96.36%。

The object-oriented classification of high resolution images withspectrum analysis

With the improvement of remote sensing image spatial resolution, the spatial information of ground objects is more abundant, the ground objects’ size, shape and the relationships between adjacent ground objects are reflected better. As a result, high resolution image classification methods are currently more emphasized on using spatial information and many artificial subjective factors are participated in the classification process. In the abandoned area it is difficult to interpret. Too much fine segmentation causing plenty of operations is another difficult problem of high resolution images classification. The object-oriented classification of high resolution images with spectrum analysis is put forward as follows: SAM is firstly classified roughly with mask operation, and then fine object-oriented classification is conducted. That solves better the problems of type uncertainty of ground objects and too much fine segmentation in object-oriented classification. The river and road of the Tibet naqu area are extracted with eight bands WorldView2 data of 0.5 meters spatial resolution and accuracy reaches 96.6%.