We have seen that a key benefit of using the Region Labeling Tool with hierarchical segmentations is that a much fuller utilization of the spectral and spatial information content of imagery is obtained than possible with conventional pixel-oriented analysis techniques. For multispectral or hyperspectral remotely sensing imagery data, the regions consist of groupings of image pixels that have similar data feature values.
Image segmentation is a key first step in a number of approaches to image analysis.
The group of pixels contained in each region provides a good statistical sampling of data values for more reliable labeling based on multispectral or hyperspectral feature values. In addition, the region shape can be analyzed as an additional clue for the appropriate labeling of the region. Labeling based on image region features rather than individual image pixels often gives a more satisfactory analysis result because spatial information is taken into account. However, region growing is not without its problems. With straightforward region growing, spectrally similar but spatially disjoint regions are never associated together, thus complicating their identification.