In the study, we propose an efficient projection-based segmentation technique for multi-spectral image. By projecting the multi-spectral image onto a referenced subspace, we transform the multi-dimensional (MD) image data into one-dimensional (1D) projection length. One proper referenced subspace with the largest deviation from the mean image signature vector is suggested in the study. After the transformation, any efficient 1D segmentation technique, such as moment-preserving method, can be applied. In the segmentation, we perform the projection procedure recursively and partition the image into proper regions according to their spectral characteristics. Then, to get a more integrated segmentation result, we adopt N nearest neighbor rule to merge the image regions according to their spatial correlation. Simulation results performed on SPOT and Landsat images have demonstrated the efficiency of the proposed approach. In addition, the segmentation result is suitable for indexing of satellite image databases
['Lena Chang', 'Chao-An Cheng', 'Jian-Liung Chen']
A novel segmentation technique using eigen space projection for satellite image indexing