Feature Selection using Teaching-Learning-Based Optimization Algorithm for Classification of Remote Sensing Images

This paper investigates feature selection for data dimensionality reduction in the binary classification of high-resolution remote sensing images. For this aim, a Teaching-Learning-Based Optimization (TLBO) algorithm is used within a wrapper feature selection (WFS) framework to pick the optimal features for the classification. In this work, the TLBO is used in conjunction with the support vector machine (SVM) classifier to constitute a machine learning paradigm. Compared to other evolutionary optimization algorithms, the proposed TLBO framework is characterized by less computational effort and no algorithm-specific parameter requirements. In the end, experimental tests are conducted to show the effectiveness of the proposed TLBO-based WFS approach by using multi-spectral data from Earth Observing-One Advanced Land Imager. The comparative results demonstrate the advantages of the proposed TLBO algorithm in achieving high classification accuracy in comparison to the genetic algorithm (GA) and particle swarm optimization (PSO).