Dr. Guillermo A. Castilla Profile

Dr. Guillermo A. Castilla

at Univ of Calgary

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Proceedings Article | 13 February 2004 Paper

Size-constrained region merging (SCRM): a new segmentation method to derive a baseline partition for object-oriented classification

Guillermo Castilla, Agustin Lobo, Joaquin Solana

Proceedings Volume 5239, (2004) https://doi.org/10.1117/12.511568

KEYWORDS: Image segmentation, Remote sensing, Image filtering, Image processing algorithms and systems, Image analysis, Image processing, Image classification, Ecology, Fractal analysis, Multispectral imaging

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Object-oriented analysis of RS images for landcover mapping is based upon the same hierarchical patch model used in modern Landscape Ecology. In such model, each patch is a loosely integrated whole -- an object that can be viewed simultaneously as part of a superobject and as made of subobjects. The focal level, i.e. the level of the nested hierarchy on which the analysis is focused, is indicated by the minimum size that the objects of this level are supposed to have. Based on this framework, we have developed a segmentation method that defines a partition on a multispectral image such that each segment exceeds the minimum size required for patches of the focal level. The segmented image is subsequently used as the baseline for an object-oriented classification in which segments become the basic units. In our contribution we briefly describe the method, focusing on its region merging stage. The most distinctive feature of the latter is that while the merging sequence is ordered by increasing dissimilarity as in conventional methods, there is no need to define a threshold on the dissimilarity measure between adjacent regions. The initial segments are image blobs (defined here as tiny homogeneous regions, darker, brighter or of different hue than their surroundings), contoured by a morphological method (gradient watersheds). The merging process is conducted iteratively, allowing only one mergence per segment and iteration, and not allowing mergence when (1) one of the two segments to be merged has a neighbor that has been merged in current iteration; (2) both segments exceed the minimum size; and (3) one of both segments is smaller than this size but it has a more similar neighbor than the one under consideration. The method is illustrated with an example on a forested region in Spain.

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