Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification

Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote sensing analyses report the results of the feature selection process to provide insights on important feature variable for future analyses. Are these feature selection results generalizable to other classification models, or are they specific to the input dataset and classification model they were derived from? To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models using different training sets acquired from the same remotely sensed image, and to classification models of other similar remotely sensed imagery. Feature selection results for various training sets acquired from the same image and different images widely varied on small training sets (n = 108). Variability in feature selection results between training sets acquired from different images was reduced as training set size increased; however, each RFE-derived feature set was unique, even when training sample size was increased over 10-fold (n = 1895). The transferability of an RFE-derived feature set from a high performing classification model was, on average, slightly more accurate in comparison to other classification models of the same image, but provided, on average, slightly lower accuracies when generalized to classification models of other, similar remotely sensed imagery. However, the effects of feature set transferability on classification accuracy were inconsistent and varied per classification model. Specific feature selection results in other classification models or remote sensing analyses, while useful for providing general insights on feature variables, may not always generalize to provide comparable accuracies for other classification models of the same dataset, or other, similar remotely sensed datasets. Thus, feature selection should be individually conducted for each training set within an analysis to determine the optimal feature set for the classification model.

Automated summary

Feature selection is important in remote sensing analysis to improve classification accuracy and reduce computational complexity. However, the generalizability and transferability of feature selection results depend on different classification models and datasets. While feature selection results can provide insights for analysis, they may not always provide comparable accuracies when applied to other classification models or similar remotely sensed datasets. Therefore, feature selection should be individually conducted for each training set to determine the optimal feature set for the classification model.