一种光谱与纹理特征加权的高分辨率遥感纹理分割算法

高分辨率遥感影像呈现极其丰富的光谱和结构信息,传统的基于光谱的遥感影像分割方法往往使得分割区域过于细碎且分割精度不高.尝试将纹理信息引入到特征空间以期解决该问题.本文算法中,特征空间由光谱和纹理两类构成,并采用加权最小距离分类器.光谱信息通过对原始影像的变带宽均值漂移滤波获得,纹理信息由对原始影像逐波段采用多尺度伽博(Gabor)滤波器组滤波获得;依据训练样区中各特征维的方差确定该地物类别分类时特征维的权重,并通过训练样区的特征加权平均获得各地物类别的聚类中心;最后,将像素点归为到加权聚类中心距离最小的类别.实验结果表明,提出的均值漂移带宽确定方法是有效的,加权融合算法较基于光谱的分割方法在分割精度上有一定程度的提高.

A Segmentation Algorithm for High-Resolution Remote Sensing Texture Based on Spectral and Texture Information Weighting

High-spatial-resolution remote sensing imagery provides a large amount of spectral and structure information. However, their availability also poses challenges to conventional spectral segmentation methods, and the segmenation region is often too fragmentary and has low accuracy. In order to overcome this inadequacy, texture information is introduced into spectral feature space. In the algorithm, the new feature space consists of spectral and texture elements, and weighted minimum distance classifier is designed. Firstly, spectral feature is got by a variable bandwidth mean shift filtering procedure on original images, and texture feature is got by convolving original image with multiscale Gabor filter bank band by band. Secondly, the weight of certain feature dimension for a certain land class is determined by its deviation in the land class training area. Then, the clustering centre is also calculated by averaging weighted feature vectors in the training area. Finally, every pixel is classified into the class with nearest weighted distance. The experiments demonstrate that the presented band definition method using the variable mean shift filtering is effective and the combination of different features can achieve better performance than only using texture or spectral feature independently.