Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

By Yuguo Qian, Weiqi Zhou, Jingli Yan, Weifeng Li, Lijian Han. Remote Sensing, 2014

376 citations recorded by the source corpus.

This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with different training sample sizes. We found that: (1) SVM and NB were superior to CART and KNN, and both could achieve high classification accuracy (>90%); (2) the setting of tuning parameters greatly affected classification accuracy, particularly for the most commonly-used SVM classifier; the optimal values of tuning parameters might vary slightly with the size of training samples; (3) the size of training sample also greatly affected the classification accuracy, when the size of training sample was less than 125. Increasing the size of training samples generally led to the increase of classification accuracies for all four classifiers. In addition, NB and KNN were more sensitive to the sample sizes. This research provides insights into the selection of classifiers and the size of training samples. It also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing these parameters.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/6500e3512c8c266561d016902a3904f66d453624

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Shahfahad
2. Susanta Mahato
3. Martin Kappas
4. Atiqur Rahman
5. Swapan Talukdar
6. Phan Thanh Noi

In Ecology

1. Martin Kappas
2. Phan Thanh Noi

In Media Technology

1. Martin Kappas
2. Bahram Salehi
3. Phan Thanh Noi

Citations in the corpus

• Remote sensing for wetland classification: a comprehensive review (2018, methodology): “Several studies, including Duro, Franklin, and Dubé (2012), Qian et al. (2014), and Adam et al. (2014), have evaluated the accuracy of the SVM classifier for wetland classification.”
• Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery (2017, methodology): “, it is the key tuning parameter of kNN [41].”
• Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, methodology): “A small difference is found between the previous study and the present study on the accuracy levels of ANN, SAM, fuzzy ARTMAP and SVM [75,100,101].”