Image segmentation has been an important research area in image analysis and interpretation.An ideal segmentation strategy of remotely sensed data should consider problems of over-segmentation and under-segmentation simultaneously and find a good tradeoff between them.In this paper

image segmentation for IKONOS multispectral data is investigated by using techniques of mathematical morphology

and a novel hybrid segmentation algorithm is proposed by combining both edge and texture features of images.Based on the K-L transform of multispectral data

edge features are detected by morphological multiscale and multidirection gradient algorithms

and image objects are marked through morphological filtering and local variance features extracting.Finally

the marker controlled watershed algorithm is implemented.The results indicate that the performance of the proposed algorithm is superior to the gradient based watershed segmentation.Moreover

this approach is more suitable for high resolution remotely sensed data to overcome over-segmentation and under-segmentation problems effectively.