A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

2017 ◽  
Vol 38 (23) ◽  
pp. 7176-7195 ◽  
Author(s):  
Qiong Wu ◽  
Ruofei Zhong ◽  
Wenji Zhao ◽  
Han Fu ◽  
Kai Song
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Decision Tree ◽  
Land Cover Classification ◽  
Airborne Lidar ◽  
Aerial Images ◽  
Support Vector ◽  
Lidar Data ◽  
Object Based ◽  
Airborne Lidar Data

scholarly journals Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification

2020 ◽  
Vol 9 (5) ◽  
pp. 329 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Vincent Nyirenda ◽  
Yuji Murayama ◽  
Manjula Ranagalage
Keyword(s):  
Land Cover ◽  
Decision Tree ◽  
Land Cover Classification ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Minimum Number ◽  
Tree Algorithms ◽  
And Performance

Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine).

scholarly journals Automatic CORINE land cover classification from airborne LIDAR data

2018 ◽  
Vol 126 ◽  
pp. 186-194 ◽  
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Lucía Díaz-Vilariño ◽  
Luis M. González-deSantos
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Corine Land Cover ◽  
Airborne Lidar Data

scholarly journals Support Vector Machine for Land Cover Classification using Lidar Data

2021 ◽  
Vol 873 (1) ◽  
pp. 012095
Author(s):  
M I Hariyono ◽  
Rokhmatuloh ◽  
M P Tambunan ◽  
R S Dewi
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Prediction Model ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Surface Model ◽  
Lidar Data ◽  
Topographic Map

Abstract The Lidar technology is widely used in various studies for mapping needs. In this study was to extract land cover using Lidar data by incorporating a support vector machine (SVM) approach. The study was located in the city of Lombok, Nusa Tenggara Barat. Image extraction was performed on single wavelength Lidar data to produce intensity and elevation (Digital Surface Model) features. Feature extraction of Lidar data was implemented by using a pixel-based approach. The extracted features used as an attribute for training data to generate the SVM prediction model. The prediction model to predict the types of land cover in the study area such as buildings, trees, roads, bare soil, and low vegetations. For accuracy assessment purposes, we used topographic map available in shapefile format as the reference map and estimated the accuracies of the resulted classifications. In this study, land cover classification used combination bands which improved the overall accuracy by approximately 20%. The use of the intensity data in this band combination was the reason for the increasing accuracy.

Sign in / Sign up

Export Citation Format

Share Document