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

Bibliographic Data

ID22034760
AuthorsDarius Phiri (0000-0001-9593-4970, Copperbelt University, corresponding author), Matamyo Simwanda (0000-0001-6363-7410, Copperbelt University), Vincent R Nyirenda (0000-0002-3506-3912, Copperbelt University), Yumi Murayama (0000-0003-4397-6882, University of Tsukuba), Manjula Ranagalage (0000-0001-7540-0103, University of Tsukuba)
Year2020
Volume9
Issue5
Pages329
Publication date2020-05-19
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9050329
OpenAlexW3026940285
LanguageEN
Citations received6
References cited46

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)

Algorithm · Contextual image classification · Data mining · Decision tree · Decision tree learning · Land cover · Land use · Machine learning · Overfitting · Statistical classification · Computer Science · Engineering · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Remote-Sensing Image Classification · Artificial Intelligence

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