Next Article in Journal
A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration
Next Article in Special Issue
Geometric Refinement of ALS-Data Derived Building Models Using Monoscopic Aerial Images
Previous Article in Journal
Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements
Previous Article in Special Issue
Generating Topographic Map Data from Classification Results
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 243; doi:10.3390/rs9030243

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
Academic Editors: Norman Kerle, Markus Gerke, Sébastien Lefèvre and Prasad S. Thenkabail
Received: 31 December 2016 / Revised: 21 February 2017 / Accepted: 2 March 2017 / Published: 5 March 2017
View Full-Text   |   Download PDF [32422 KB, uploaded 5 March 2017]   |  

Abstract

Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint. View Full-Text
Keywords: oversegmentation; runtime; OBIA; computer vision; pixels; superpixels; random forest oversegmentation; runtime; OBIA; computer vision; pixels; superpixels; random forest
Figures

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

Csillik, O. Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels. Remote Sens. 2017, 9, 243.

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.

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