The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-2/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W1, 19–23, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W1, 19–23, 2013

  13 May 2013

13 May 2013


L.-H. Hsiao and K.-S. Cheng L.-H. Hsiao and K.-S. Cheng
  • Dept. of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan

Keywords: Remote Sensing, Landuse Classification, Uncertainties, Bootstrap Resampling

Abstract. Multispectral remote sensing images are widely used for landuse/landcover (LULC) classification. Performance of such classification practices is normally evaluated through the confusion matrix which summarizes the producer’s and user’s accuracies and the overall accuracy. However, the confusion matrix is based on the classification results of a set of multi-class training data. As a result, the classification accuracies are heavily dependent on the representativeness of the training data set and it is imperative for practitioners to assess the uncertainties of LULC classification in order for a full understanding of the classification results. In addition, the Gaussian-based maximum likelihood classifier (GMLC) is widely applied in many practices of LULC classification. The GMLC assumes the classification features jointly form a multivariate normal distribution, whereas as, in reality, many features of individual landcover classes have been found to be non-Gaussian. Direct application of GMLC will certainly affect the classification results. In a pilot study conducted in Taipei and its vicinity, we tackled these two problems by firstly transforming the original training data set to a corresponding data set which forms a multivariate normal distribution before conducting LULC classification using GMLC. We then applied the bootstrap resampling technique to generate a large set of multi-class resampled training data from the multivariate normal training data set. LULC classification was then implemented for each resampled training data set using the GMLC. Finally, the uncertainties of LULC classification accuracies were assessed by evaluating the means and standard deviations of the producer’s and user’s accuracies of individual LULC classes which were derived from a set of confusion matrices. Results of this study demonstrate that Gaussian-transformation of the original training data achieved better classification accuracies and the bootstrap resampling technique is a very helpful tool for assessing uncertainties of LULC classification. The uncertainties in classification accuracies were also found to be affected by the sizes of class-specific training samples.