Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data

Abstract

This paper present the results of a support vector machine (SVM) technique and a genetic algorithm (GA) technique using generalization error bounds derived for SVMs as fitness functions (SVM/GA) for feature selection using hyperspectral data. Results obtained with the SVM/GA‐based technique were compared with those produced by random forest‐ and SVM‐based feature selection techniques in terms of classification accuracy and computational cost. The classification accuracy using SVM‐based feature selection was 91.89%. The number of features selected was 15. For comparison, the accuracy produced by the use of the full set of 65 features was 91.76%. The level of classification accuracy achieved by the SVM/GA approach using 15 features varied from 91.87% to 92.44% with different fitness functions but required a large training time. The performance of the random forest‐based feature selection approach gave a classification accuracy of 91.89%, which is comparable to the accuracy achieved by using the SVM and SVM/GA approaches using 15 features. A smaller computational cost is a major advantage associated with the random forest‐based feature selection approach. The training time for the SVM‐based approach is also very small in comparison to the SVM/GA approach, thus suggesting the usefulness of random forest‐ and SVM‐based feature selection approaches in comparison to the SVM/GA approach for land cover classification problems with hyperspectral data. Further, a higher classification accuracy was achieved with a combination of 20 selected features in comparison to the level of accuracy obtained using 15 features, but the difference in accuracy was not significant. To validate the results, SVM‐, SVM/GA‐ and random forest‐based feature selection approaches were compared with a maximum noise transformation based feature extraction technique. Results show an improved performance using these techniques in comparison to the maximum noise transformation‐based feature extraction technique.

Acknowledgment

The DAIS data were collected and processed by DLR and were kindly made available by Professor J. Gumuzzio of the Autonomous University of Madrid, Spain. Special thanks are due to Professor Paul Mather for his critical comments on the initial draft of this paper. Comments from two anonymous referees that helped to improve the quality of this paper are also acknowledged.