Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China
- 1 September 2006
- journal article
- research article
- Published by Taylor & Francis in International Journal of Remote Sensing
- Vol. 27 (18), 4039-4055
- https://doi.org/10.1080/01431160600702632
Abstract
Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land‐cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach..Keywords
This publication has 31 references indexed in Scilit:
- Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready informationISPRS Journal of Photogrammetry and Remote Sensing, 2004
- Unsupervised classification of hyperspectral data: an ICA mixture model based approachInternational Journal of Remote Sensing, 2004
- An evaluation of per-parcel land cover mapping using maximum likelihood class probabilitiesInternational Journal of Remote Sensing, 2003
- Segmenting multispectral Landsat TM images into field unitsIEEE Transactions on Geoscience and Remote Sensing, 2002
- Application of spectral mixture analysis for terrain evaluation studiesInternational Journal of Remote Sensing, 2000
- Feature selection and land cover classification of a MODIS-like data set for a semiarid environmentInternational Journal of Remote Sensing, 1999
- Thermal inertia determination from space— a tutorial reviewInternational Journal of Remote Sensing, 1996
- Seeded region growingIEEE Transactions on Pattern Analysis and Machine Intelligence, 1994
- A review of assessing the accuracy of classifications of remotely sensed dataRemote Sensing of Environment, 1991
- Region Extraction in SPOT DataGeocarto International, 1988