Abstract
The increased feature space available in object-based classification environments (e.g., extended spectral feature sets per object, shape properties, or textural features) has a high potential of improving classifications. However, the availability of a large number of derived features per segmented object can also lead to a time-consuming and subjective process of optimizing the feature subset. The objectives of this study are to evaluate the effect of the advanced feature selection methods of popular supervised classifiers (Support Vector Machines (SVM) and Random Forest (RF)) for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery, in order to optimize their usage for object-based agriculture pattern recognition tasks. In this study, several advanced feature selection methods were divided into both types of classifiers (SVM and RF) to conduct further evaluations using five feature-importance-evaluation methods and three feature-subset-evaluation methods. A visualization method was used to measure the change pattern of mean classification accuracy with the increase of features used, and a two-tailed t-test was used to determine the difference between two population means for both repeated ten classification accuracies. This study mainly contribute to the uncertainty analysis of feature selection for object-based classification instead of the per-pixel method. The results highlight that the RF classifier is relatively insensitive to the number of input features, even for a small training set size, whereby a negative impact of feature set size on the classification accuracy of the SVM classifier was observed. Overall, the SVM Recursive Feature Elimination (SVM-RFE) seems to be an appropriate method for both groups of classifiers, while the Correlation-based Feature Selection (CFS) is the best feature-subset-evaluation method. Most importantly, this study verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA): It is highly advisable for feature selection to be performed before object-based classification, even though an adverse impact could sometimes be observed from the wrapper methods.
Highlights
Feature selection is considered an important step within a classification process because it improves the performance of the classifier and reduces the complexity of the computation by removing redundant information [1]
The comparison of feature selection methods for object-based classification was divided into two parts within this study, due to the different types of results obtained from the feature selection process, including the analysis of feature-importance-evaluation methods and feature-subset-evaluation methods
Regarding the feature-importance-evaluation methods, five algorithms (Gain ratio, Chi-square, Support Vector Machines (SVM) Recursive Feature Elimination (SVM-RFE), Relief-F and Random Forest) were used to obtain the ranked list of the features, and each feature was added individually for classification according to the ranking list
Summary
Feature selection is considered an important step within a classification process because it improves the performance of the classifier and reduces the complexity of the computation by removing redundant information [1]. An additional challenge is avoiding the time-consuming step of calculating all available features and the subjective process of artificial feature selection when determining optimal features, besides some other specific issues of object-based image analysis (e.g., object scale and training set size) [8,9]. The splitting rule for the decision tree was formerly used as the attribute selection measure [13] and has been used in several studies to train the decision tree model, while decision tree classifiers were widely applied to object-based image analysis [14,15]. Novack et al [2] used four advanced feature selection algorithms to identify the most relevant features for the classification of a high-resolution image but did not further assess these methods and their respective performance relative to each other
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