Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data

Abstract

Interest in the joint use of different data from multiple sensors has been increased for classification applications. This is because the fusion of different information can produce a better understanding of the observed site. In this field of study, the fusion of light detection and ranging (LIDAR) and passive optical remote sensing data for classification of land cover has attracted much attention. This paper addressed the use of a combination of hyperspectral (HS) and LIDAR data for land cover classification. HS images provide a detailed description of the spectral signatures of classes, whereas LIDAR data give detailed information about the height but no information for the spectral signatures. This paper presents a multiple fuzzy classifier system for fusion of HS and LIDAR data. The system is based on the fuzzy K-nearest neighbor (KNN) classification of two data sets after application of feature grouping on them. Then a fuzzy decision fusion method is applied to fuse the results of fuzzy KNN classifiers. An experiment was carried out on the classification of HS and LIDAR data from Houston, USA. The proposed fuzzy classifier ensemble system for HS and LIDAR data provide interesting conclusions on the effectiveness and potentials of the joint use of these two data. Fuzzy classifier fusion on these two data sets improves the classification results when compared with independent single fuzzy classifiers on each data set. The fuzzy proposed method represented the best accuracy with a gain in overall accuracy of 93%.