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Search-centric, sample supervised image segmentation has been demonstrated as a viable general approach applicable within the context of remote sensing image analysis. Such an approach casts the controlling parameters of image processing—generating segments—as a multidimensional search problem resolvable via efficient search methods. In this work, this general approach is analyzed in the context of connected component segmentation. A specific formulation of connected component labeling, based on quasi-flat zones, allows for the addition of arbitrary segment attributes to contribute to the nature of the output. This is in addition to core tunable parameters controlling the basic nature of connected components. Additional tunable constituents may also be introduced into such a framework, allowing flexibility in the definition of connected component connectivity, either directly via defining connectivity differently or via additional processes such as data mapping functions. The relative merits of these two additional constituents, namely the addition of tunable attributes and data mapping functions, are contrasted in a general remote sensing image analysis setting. Interestingly, tunable attributes in such a context, conjectured to be safely useful in general settings, were found detrimental under cross-validated conditions. This is in addition to this constituent’s requiring substantially greater computing time. Casting connectivity definitions as a searchable component, here via the utilization of data mapping functions, proved more beneficial and robust in this context. The results suggest that further investigations into such a general framework could benefit more from focusing on the aspects of data mapping and modifiable connectivity as opposed to the utility of thresholding various geometric and spectral attributes.

On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework