Landslides are considered one of the most important natural hazards. Mapping landslides and producing landslide inventory maps have received special attention from a wide range of specialists. The main objective of this study was to produce landslide inventory maps using advanced pixel-based (ANN and SVM) and object-oriented approaches. The most important challenge in this case is to determine the optimum structure of classification methods. The Taguchi method was to perform optimization of the structure of ANN and SVM and segmentation process in the object-oriented classification method. Results showed that the Taguchi method can be effectively used to cope with this problem. It significantly reduces the number of classification tests. We also showed that there were no significant differences existed between ANN and SVM approaches (χ2 value of 3.33). However, we demonstrated that object-oriented approaches significantly outperformed the pixel-based classification methods (Z value of 5.70) in producing a landslide inventory map. The accurate map produced using an object-oriented approach (overall accuracy of 0.90) effectively determines the shape of landslides and also efficiently shows the intensifying effects of land use changes in the occurrence of landslides. © 2013 Elsevier B.V.
- Landslide inventory
- Object-oriented classification
- Pixel-based classification
- Taguchi method