A Segmentation Method for High Spatial Resolution Remote Sensing Images Based on the Fusion of Multifeatures

A novel method based on the fusion of spectral, texture, and shape features is proposed for the segmentation of high spatial resolution remote sensing images. The method uses the region merging idea to get the final segmentation result on the basis of initial segmentation. Texture features of the regions are obtained by the nonsubsampled contourlet transform. An integrated region merging criterion is built by combining the texture, spectral, and shape features. To ensure the efficiency of the method, the region adjacency graph and the nearest neighbor graph are used to maintain the adjacency relations in the region merging stage. The global optimization strategy is adopted to realize the image segmentation gradually. Two experiments are performed to verify the effectiveness of the proposed method. One experiment validates the influence of the merging criteria with different feature combinations on the segmentation results. Another experiment compares the effect of the method with the segmentation methods embedded in ENVI and eCognition. Experimental results show that the method can make full use of the features of the images to achieve accurate and efficient segmentations.