Nested-hierarchical scene models and image segmentation

Publisher Website
Google Scholar
Abstract
The objective of image segmentation in remote sensing is to define regions in an image that correspond to objects in the ground scene. Traditional scene models underlying image segmentation procedures have assumed that objects as manifest in images have internal variances that are both low and equal. This scene model is unrealistically simple. An alternative scene model recognizes different scales of objects in scenes. Each level in the hierarchy is nested, or composed of objects or categories of objects from the preceding level. Different objects may have distinct attributes, allowing for relaxation of assumptions like equal variance. A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modelled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while inter-region distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions. Application of the segmentation algorithm for forest stand delineation in Landsat TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely-stocked forest stands which have high internal variance.

This publication has 22 references indexed in Scilit: