Image segmentation transforms pixel-level information from raw images to a higher level of abstraction in which related pixels are grouped into disjoint spatial regions. Such regions typically correspond to natural or man-made objects or structures, natural variations in land
cover, etc. For many image interpretation tasks (such as land use assessment, automatic target cueing, defining relationships between objects, etc.), segmentation can be an important early step.
Remotely sensed images (e.g., multi-spectral and hyperspectral images) often contain many spectral bands (i.e., multiple layers of 2D images). Multi-band images are important because they contain more information than single -band images. Objects or natural variations
that are readily apparent in certain spectral bands may be invisible in 2D broadband images. In this paper, the classical region growing approach to image segmentation is generalized from single to multi-band images. While it is widely recognized that the quality of image segmentation is affected by which segmentation algor ithm is used, this paper shows that algorithm parameter values can have an even more profound effect. A novel self-calibration framework is developed
for automatically selecting parameter values that produce segmentations that most closely resemble a calibration edge map (derived separately using a simple edge detector). Although the
framework is generic in the sense that it can imbed any core segmentation algorithm, this paper only demonstrates self-calibration with multi-band region growing. The framework is applied to
a variety of AVIRIS image blocks at different spectral resolutions, in an effort to assess the impact of spectral resolution on segmentation quality. The image segmentations are assessed
quantitatively, and it is shown that segmentation quality does not generally appear to be highly correlated with spectral resolution.