Abstract
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).
Highlights
Object based image analysis (OBIA) has become an effective method of land cover classification of remotely sensed data [1,2]
This study has shown that Decision tree (DT) algorithms are effective tools in developing decision rulesets for land cover thematic maps
The main focus was selecting DT algorithms which have high classification accuracies, simple structures, and are easy to interpret, by using the multiple criteria approach suggested by DeFries et al [47]
Summary
Object based image analysis (OBIA) has become an effective method of land cover classification of remotely sensed data [1,2]. Unlike traditional pixel-based analysis, OBIA offers an opportunity to develop discrete objects which relate to real world objects through image segmentation [3,4]. The segmentation process reduces within class spectral variations and offers an opportunity to increase classification accuracy, especially when conducted at an appropriate scale [5,6]. The ability to incorporate texture, compaction, and other object related information with spectral information has differentiated OBIA from other methods of classification such as pixel and sub-pixel approaches. There are currently a number of segmentation algorithms available in eCognition Developer; multiresolution segmentation is the most common method used in land cover classification [1,4,9]
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