Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification
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 pro...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
|
Series: | ISPRS International Journal of Geo-Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2220-9964/9/5/329 |
id |
doaj-2769f888511247a2be49bc40dee92497 |
---|---|
record_format |
Article |
spelling |
doaj-2769f888511247a2be49bc40dee924972020-11-25T03:04:06ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-05-01932932910.3390/ijgi9050329Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover ClassificationDarius Phiri0Matamyo Simwanda1Vincent Nyirenda2Yuji Murayama3Manjula Ranagalage4Deptment of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University, P.O. BOX 21692 Kitwe, ZambiaDeptment of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University, P.O. BOX 21692 Kitwe, ZambiaDepartment of Zoology and Aquatic Sciences, School of Natural Resources, Copperbelt University, P.O. BOX 21692 Kitwe, ZambiaFaculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8572, JapanFaculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8572, JapanDecision 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).https://www.mdpi.com/2220-9964/9/5/329land useremote sensingspectral mixture analysischange detectionoptical imagesAfrica |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Darius Phiri Matamyo Simwanda Vincent Nyirenda Yuji Murayama Manjula Ranagalage |
spellingShingle |
Darius Phiri Matamyo Simwanda Vincent Nyirenda Yuji Murayama Manjula Ranagalage Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification ISPRS International Journal of Geo-Information land use remote sensing spectral mixture analysis change detection optical images Africa |
author_facet |
Darius Phiri Matamyo Simwanda Vincent Nyirenda Yuji Murayama Manjula Ranagalage |
author_sort |
Darius Phiri |
title |
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification |
title_short |
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification |
title_full |
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification |
title_fullStr |
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification |
title_full_unstemmed |
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification |
title_sort |
decision tree algorithms for developing rulesets for object-based land cover classification |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2020-05-01 |
description |
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). |
topic |
land use remote sensing spectral mixture analysis change detection optical images Africa |
url |
https://www.mdpi.com/2220-9964/9/5/329 |
work_keys_str_mv |
AT dariusphiri decisiontreealgorithmsfordevelopingrulesetsforobjectbasedlandcoverclassification AT matamyosimwanda decisiontreealgorithmsfordevelopingrulesetsforobjectbasedlandcoverclassification AT vincentnyirenda decisiontreealgorithmsfordevelopingrulesetsforobjectbasedlandcoverclassification AT yujimurayama decisiontreealgorithmsfordevelopingrulesetsforobjectbasedlandcoverclassification AT manjularanagalage decisiontreealgorithmsfordevelopingrulesetsforobjectbasedlandcoverclassification |
_version_ |
1724682874736607232 |