Next Article in Journal
Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks
Next Article in Special Issue
Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk
Previous Article in Journal
Estimating the Available Sight Distance in the Urban Environment by GIS and Numerical Computing Codes
Previous Article in Special Issue
GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes
Open AccessArticle

A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis

1
Université Bretagne Sud, IRISA, 56017 Vannes, France
2
DYNAFOR, University of Toulouse, INRA, 31326 Castanet-Tolosan, France
3
Université Côte d’Azur, INRIA, TITANE team, 06902 Sophia Antipolis, France
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(2), 70; https://doi.org/10.3390/ijgi8020070
Received: 30 July 2018 / Revised: 23 January 2019 / Accepted: 27 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue GEOBIA in a Changing World)
The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data are available. To reduce the sensitivity of the GEOBIA process to the segmentation step, here we consider that a set of segmentation maps can be derived from remote sensing data. Inspired by the ensemble paradigm that combines multiple weak classifiers to build a strong one, we propose a novel framework for combining multiple segmentation maps. The combination leads to a fine-grained partition of segments (super-pixels) that is built by intersecting individual input partitions, and each segment is assigned a segmentation confidence score that relates directly to the local consensus between the different segmentation maps. Furthermore, each input segmentation can be assigned some local or global quality score based on expert assessment or automatic analysis. These scores are then taken into account when computing the confidence map that results from the combination of the segmentation processes. This means the process is less affected by incorrect segmentation inputs either at the local scale of a region, or at the global scale of a map. In contrast to related works, the proposed framework is fully generic and does not rely on specific input data to drive the combination process. We assess its relevance through experiments conducted on ISPRS 2D Semantic Labeling. Results show that the confidence map provides valuable information that can be produced when combining segmentations, and fusion at the object level is competitive w.r.t. fusion at the pixel or decision level. View Full-Text
Keywords: GEOBIA; segmentation fusion; segmentation evaluation; consensus; remote sensing GEOBIA; segmentation fusion; segmentation evaluation; consensus; remote sensing
Show Figures

Figure 1

MDPI and ACS Style

Lefèvre, S.; Sheeren, D.; Tasar, O. A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis. ISPRS Int. J. Geo-Inf. 2019, 8, 70.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop