Next Article in Journal
Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain
Previous Article in Journal
Droughts and Floods in the La Plata Basin in Soil Moisture Data and GRACE
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(6), 7350-7377; doi:10.3390/rs70607350

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

German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Academic Editors: Arko Lucieer and Prasad S. Thenkabail
Received: 17 March 2015 / Revised: 11 May 2015 / Accepted: 27 May 2015 / Published: 3 June 2015
View Full-Text   |   Download PDF [8699 KB, uploaded 3 June 2015]   |  

Abstract

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 processinggenerating segmentsas 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. View Full-Text
Keywords: geographic object-based image analysis; segmentation; mathematical morphology; sample supervised; spatial metrics; metaheuristics; connected component geographic object-based image analysis; segmentation; mathematical morphology; sample supervised; spatial metrics; metaheuristics; connected component
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fourie, C. On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework. Remote Sens. 2015, 7, 7350-7377.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top