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

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.