Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China

Abstract

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.. Acknowledgements The authors thank ITC, the Netherlands for funding the field work and providing the remote sensing data, thank China Remote Sensing Center for cooperation in the field work, and thank CONACYT, Mexico in supplying the fellowship from the project 2002‐C01‐0075 (Fondo Sectorial de Investigación Ambiental SEMARNAT‐CONACYT) during the writing up of this paper. Thanks also go to Msc Jose Antonio Navarrete Pacheco, from the Geographic Institute of UNAM, Mexico, for his help in geometric correction of image data. Special thanks go to Anssi Pekkarinen, from the Institute for Environment and Sustainability, Italy, for his help in references of image segmentation.