Combination techniques for hyperspectral image interpretation

In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare both methods (WLC-GA and its extension) with a more complex non-linear meta learning strategy called Stacked Generalization in which Support Vector Machines with Radial Basis Function kernel was used as combiner [2]. The experimental results, considering two widely used data sets, the Indian Pines and the Pavia University, are conducted in three different scenarios. Results show that both WLC-GA and its extended version achieve the best overall accuracy, and the proposed classification approach overcomes the accuracies of the other traditional ones used in this study.