Combining Multiple Classification Methods for Hyperspectral Data Interpretation

In the past few years, Hyperspectral image analysis has been used for many purposes in the field of remote sensing and importantly for land cover classification. Land cover classification is a challenging task and the production of accurate thematic maps is a common goal among researchers. A hyperspectral image is composed of hundreds of spectral channels, where each channel refers to a specific wavelength. Such a large amount of information may lead us to a deeper investigation of the materials on Earth's surface, and thus, a more precise interpretation of them. In this work, we aim to produce a thematic map that is more accurate by combining multiple classification methods. Three feature representations based on spectral and spatial data and two learning algorithms (Support Vector Machines (SVM) and Multilayer Perceptron Neural Network (MLP)) were used to produce six different classification methods to perform the combination. Our combining approach is based on Weighted Linear Combination (WLC), in which weights are found using a Genetic Algorithm (GA)-WLC-GA. Experiments were carried out with two well-known datasets: Indian Pines and Pavia University. In order to evaluate the robustness of the proposed combiner, experiments using different training sizes were conducted. They show promising results for both datasets for our WLC-GA proposal and are better than the widely used Majority Vote (MV) and Average rules in terms of accuracy. By using only 10% of training samples, our proposal was able to find the best weights and overcome the drawbacks of the traditional combination rules.