Pixel-based or Object-based: Which approach is more appropriate for remote sensing image classification?

We present an overall performance comparison between the two most popular remote sensing image classification approaches which are: Pixel-based and Object-based. This evaluation is conducted using different state of the art statistical measures. The analysis of the classification power associated to these most widely utilized methods is conducted on Landsat-7 ETM+ image of Algiers through support vector machines. Since the performance of the object-based classification is inherently dependent on the success of the segmentation task, we have computed the overall accuracy, the kappa coefficient, the Z-score, the F-measure coefficient, and the area under ROC curve (AUC) value for different segmentation thresholds. This quantization of the segmentation level based on the number of pixels allowed to define a region (NPR) is necessary since image segmenters (which significantly impact classification) often exploit different paradigms and therefore exhibit different error rates. Our investigation has revealed that the object-based method is more accurate than the pixel-based method in the following two scenarios: (i) in the presence of a perfect segmentation task prior to object-based classification; (ii) whenever NPR is less than 8 pixels (corresponding to 240m in the current resolution). This second case is justified by the fact that the area under the ROC curve of object-based is larger than the one in the pixel-based. However, if NPR is not used or greater than 8 pixels, then the pixel-based approach is more appropriate.