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

Abstract

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.

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

“…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.…”

“…Searching the parameter space efficiently was a major driver for the development of this method [26]. Metaheuristics, which are stochastic population based search methods, are well suited and studied in the context of this general approach [26,29,42,43], commonly leading to higher quality fitness scores in less search time compared to more general search strategies. Such a general approach may also be used to compare segmentation algorithms for a given task, or purely to test the general feasibility of a given algorithm for a given task.…”

“…Specific aspects investigated include the evaluation of the performances of different search methods [29,[42][43][44], the applicability of various empirical discrepancy metrics (fitness functions) [29,36], the performances of various segmentation algorithms in such an approach [29,42,[45][46][47] and the extension of the concept to more modular image processing methods [24,[45][46][47][48][49][50]. Uncertainties remain surrounding the generalizability of such a method, its sampling size requirements and whether strong correlations exist between classification results and segmentation [27,29]. Research and freeware software in this vein are available [29,42,51].…”

“…The proposed method is compared with the generic formulation of sample supervised segment generation and results are demonstrated via the task of accurately segmenting structures in towns, villages and refugee camps on VHR optical remote sensing data. This contribution thus falls within the context of enlarged search spaces first presented in [29], but proposes a methodology that uses spectral content contained within reference segments as opposed to adding data transformation or mapping functions.…”

“…Various general approaches have been proposed to address the challenge of thematically accurate image segmentation and classification (semantic segmentation), including advocating rule-set or expert system's approaches within GEOBIA [1,14], the development of new domain specific segmentation algorithms [18], multi-scale image analysis [6,19,20], using context information or spatial relationships among segments [14,21,22], and hybridizing or interleaving classification and segmentation processes [23][24][25]. Another general approach addressing the problem of segmentation within GEOBIA casts the creation of thematically accurate image segments as a search or optimization problem [26][27][28][29]. In such an approach the parameters of a given segmentation algorithm is automatically tuned based on the provision of a limited amount of user provided reference segments.…”