Paper Info
Title
Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation
Abstract
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 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.
Year
DOI
Venue
2014
10.3390/rs61211852
REMOTE SENSING
Keywords
Field
DocType
geographic object-based image analysis,segmentation,classification,sample supervised,spatial metrics,metaheuristics
Data mining,Pattern recognition,Computer science,Segmentation,Spectral data,Hybrid image,Artificial intelligence,Search problem,Classifier (linguistics),Metaheuristic
Journal
Volume
Issue
Citations 
6
12
1
PageRank 
References 
Authors
0.35
32
2
Name
Order
Citations
PageRank
Christoff Fourie171.88
Elisabeth Schoepfer2185.99