光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法

针对高分辨率遥感影像具有较为丰富的地物属性“谱相”信息和空间分布及其组合“图式”信息的特点,提出了一种光谱和形状特征相结合的建筑物自动提取方法。在多尺度分割和矢量化基础上,根据建筑物的形状、光谱特征,从特征基元中自动选取样本,并计算其特征;通过根据建筑物形状、光谱、纹理构造的模板,在整景影像上进行建筑区域识别,并在建筑区域内提取建筑物外部轮廓。实验表明,本算法具有较高的识别率和较低的误识别率。

Building Extraction from High Resolution Remote Sensing Imagery Based on Spatial-Spectral Method

As is known to all,high resolution remote sensing imagery contains plenty of spectral and spatial information which represent objects’ inner physical attributes and outer distribution and composite structure respectively.According to its characteristics mentioned above,we proposed an building extraction method based on spatial-spectral information on high resolution remote sensing imagery.On the basis of feature unit extraction through multi-scale segmentation and vectorization,we selected building samples automatically from feature units according to characteristics like shape and spectral information.Then,we preceded the building area recognition on the whole image using the template constructed by buildings’ shape,and done edge detection and thinning inside the building regions to extract the outline of building.Last,we implemented the algorithm mentioned above,and proved its validity through experiments.