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Pyramidal segmentation using higher-order local auto-correlations and its applications to Landsat forestry data

The goal of image segmentation is to partition an image into regions that are internally homogeneous and heterogeneous with respect to neighbouring regions. Recently, a link shifting based pyramidal segmentation method was proposed to resolve existing problems with elongated regions. In this paper, we propose further improvements by replacing pixel intensities at the base level with pixel level higher order local auto-correlation (HLAC) feature vectors over greyscale, RGB, and CIV channels. Thereby, rich texture-like information is incorporated into segmentation. We propose a normalized distance formula between HLAC vectors, where each component contributes with physically same unit. The new algorithms were tested on a set of Landsat images over forested areas, and compared with a non-HLAC variant and several other existing segmentation algorithms. A significant improvement in segmentation quality was achieved compared to non-HLAC variants, and it also gave better results than other existing algorithms on most examples.