Feature Selection for Classification of Remote Sensed Hyperspectral Images: A Filter approach using Genetic Algorithm and Cluster Validity

摘要

In this paper, we investigate the advantages of using feature selection approaches for classification of remote sensed hyperspectral images. We propose a new filter feature selection approach based on genetic algorithms (GA) and cluster validity measures for finding the best subset of features that maximizes inter-cluster and minimizes intra-cluster distances, respectivelly. Thus, using the optimal, or sub-optimal, subset of features, classifiers can build decision boundaries in an accurate way. Dunn's index metric, given a subset of features, is used to estimate how good the built clusters are. Experiments were carried out with two well-known datasets: AVIRIS - Indian Pines and ROSIS - Pavia University. Three different classifiers were used to evaluate our proposal: Support Vector Machines (SVM), Multi-layer Perceptron Neural Networks (MLP) and K-Nearest Neighbor (KNN). Moreover, we compare the performance of our proposal in terms of accuracies to other ones: the traditional Pixelwise, without feature selection/extraction, and the widely used Singular Value Decomposition Band Subset Selection (SVDSS). Experiments show that the classification methods using our feature selection approach produce a small subset of features which easily achieve enough discriminative power and their results are similars to the ones using SVDSS.