Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Abstract

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Tropical Forest and Wetland Losses and the Role of Protected Areas in Northwestern Belize, Revealed from Landsat and Machine Learning

“…Random forest is considered to be a robust algorithm with few parameters to optimize, and the effects of parameters are insignificant at high levels of trees [25,27]. The main parameters that affect the algorithm's accuracy are the number of regression trees created (t) and the number of variables used at each node in the tree (m).…”

“…There are many classification methods for mapping land-use and land-cover (LULC) with remotely sensed data. Machine-learning algorithms are growing in scientific research because they are more accurate than commonly used parametric algorithms like maximum-likelihood [25,26]. In addition, machine-learning algorithms can handle more complex data spaces and are more efficient because they do not rely on data distribution assumptions [25,27].…”

“…Machine-learning algorithms are growing in scientific research because they are more accurate than commonly used parametric algorithms like maximum-likelihood [25,26]. In addition, machine-learning algorithms can handle more complex data spaces and are more efficient because they do not rely on data distribution assumptions [25,27]. Machine-learning techniques that use ensembles of classification-such as neural network ensembles, random forests, bagging and boosting-have multiplied substantially in recent years.…”

“…The classification ensembles are combined using a weighting, error minimization, or a rule-based approach. By applying many classification iterations, these approaches generally perform better than other machine-learning algorithms, particularly in land cover classification [25].…”

“…The random forest (RF) algorithm has several advantages over other algorithms for mapping land cover change with remotely sensed data and generally performs best for this application [25]. Compared to most other algorithms, including advanced algorithms like support vector machines, RF is more computationally efficient, is less affected by outliers or nonparametric data, and has few parameters that need to be optimized to achieve high accuracy [25][26][27][28][29]. In addition, new advances in computing allow researchers to employ machine-learning algorithms with larger datasets at faster speeds [30].…”