Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

Abstract: In order to better understand and exploit the rich information content of new remotely sensed datasets, there is a need for comparative land cover classification studies. In this study, the automatic classification of a suburban area was investigated by using (1) digital aerial image data; (2) digital aerial image data and laser scanner data; (3) a high-resolution optical QuickBird satellite image; (4) high-resolution airborne synthetic aperture radar (SAR) data; and (5) SAR data and laser scanner data. A segment-based approach was applied. The classification rules for distinguishing buildings, trees, vegetated ground, and non-vegetated ground were created automatically by using permanent test field points in a training area and the classification tree method. The accuracy of the results was evaluated by using test field points in validation areas. The highest overall accuracies were obtained when laser scanner data were used to separate high and low objects: 97% in Test 2, and 82% in Test 5. The overall accuracies in the other tests were 74% (Test 1), 67% (Test 3), and 68% (Test 4). An important contributing factor for the lower accuracy in Tests 3 and 4 was the lower spatial resolution of the datasets. The classification tree method and test field points provided a feasible and automated means of comparing the classifications. The approach is well suited for rapid analyses of new datasets to predict their quality and potential for land cover classification.