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.