基于分水岭变换与空间聚类的高分辨率遥感影像面向对象分类

Object-oriented Classification of High Resolution Imagery Based on Watershed Transform and Spatial Clustering

面向对象方法已广泛应用于高分辨率遥感影像分类,提出一种结合改进分水岭变换与空间聚类的遥感影像面向对象分类新方法。首先,基于相位一致思想分析图像特征,由Gabor小波多尺度、多方向提取QuickBird全色影像的梯度信息;利用扩展最小变换与强制最小技术分别获取图像前景标识、重建相位一致梯度图像,利用改进后的分水岭变换获得分割对象。然后,提取各对象的多波段光谱特征,利用Gabor小波获取对象纹理矢量,并用独立成分分析方法进行特征选择,依次进行对象的光谱与纹理聚类。最后,通过分析对象间空间拓扑关系判断聚类后不确定对象的类别属性。实验结果表明该方法能取得较好结果,在一定程度上提高了影像分类的自动化水平。

Object-oriented classification of high spatial resolution remote sensing imagery is a very popular theme in the field of remote sensing science.A new approach of object-oriented combining improved water-shed transform with spatial clustering is proposed to classify high resolution remote sensing imagery in this paper.Firstly,gradient image is obtained by applying phase congruency model to the QuickBird panchro-matic image with log Gabor wavelet filters from multi-scale and multi-direction.Extended minima trans-form and minima imposition are used to get foreground marking of interesting objects and present gradient reconstruction,thus to achieve better segmentation using watershed transform based on these improvement measures.Secondly,spectral feature is obtained from multi-spectral remote sensing images,texture vector is achieved by Gabor wavelet and selected by Independence Component Analyses,and clustering based on the two features of objects.Finally,topological relationships between objects are fully considered in order to classify the uncertain objects after the former clustering.Results of experiments demonstrate that the new method can get desired classification results and improve the automatization of remote sensing data classifi-cation to some extent.