Automatic acquisition of hierarchical mathematical morphology procedures by genetic algorithms

Abstract:Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid-and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.

Section: Sample Supervised Segment Generationmentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

“…Generalizability to unseen data, sampling requirements, method extensions, and method integration are open topics [6,54,55]. Note that parameters may also define construction processes of lower-level building blocks for image analysis, with mathematical morphology and genetic programming well suited to such designs [56,57].…”

Section: Sample Supervised Segment Generationmentioning

confidence: 99%

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

Fourie¹

2015

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.

“…A user typically needs to digitize or provide examples of desired segmentation results. Such an approach has attracted research attention in the imaging disciplines in general [26,28,[31][32][33][34][35] and also more specifically in the context of remote sensing image analysis [27,29,36]. It is a feasible strategy if a scene contains numerous "similar" elements that are of interest, common in many mapping tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user OPEN ACCESS Remote Sens. 2014, 6 11853 provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.