Segmentation of polar scenes using multi-spectral texture measures and morphological filtering

Abstract

Two segmentation and two unsupervised classification schemes are applied to four Landsat TM Antarctic scenes. The methods include the region-growing and the region-oriented segmentation approaches and the Divide-and-Conquer and the Mahalanobis classifiers. Combinations of spectral signatures and Grey Level Difference Vector (GLDV) textural measures are computed for each of the seven TM bands. Correlation matrices then are constructed to reduce the feature vector. Means, standard deviations, and angular second moments are selected, usually for TM channels 4, 5, and 6.

In general it is found that the segmentation schemes produce results which are judged to be more reliable and useful than those obtained from the classification schemes. The region-oriented segmentation approach is found to produce the best results.

The morphological three-dimensional opening and closing operators are used as a preprocessing step in both the segmentation and classification approaches. It is found that a 7 by 7 pixel structuring element is most effective in improving both segmentation and classification results in highly complex scenes. The closing operator provides smoother boundaries between regions and more uniform regions, reducing both noise and insignificant small regions from otherwise uniform large regions. On the other hand, the opening operator is found to be effective in identifying individual ice floes within a background of broken sea ice.