INTEGRATING MULTIPLE CLASSIFIERS WITH FUZZY MAJORITY VOTING FOR IMPROVED LAND COVER CLASSIFICATION

摘要

In this paper the idea is to combine classifiers with different error types based on Fuzzy Majority Voting (FMV). Four study areas with different sensors and scene characteristics were used to assess the performance of the model. First, the lidar point clouds were filtered to generate a Digital Terrain Model (DTM), and then a Digital Surface Model (DSM) and the Normalized Digital Surface Model (nDSM) were generated. A total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar intensity image, DSM and nDSM. Three different classification algorithms were used to classify buildings, trees, roads and ground from aerial images, lidar data and the generated attributes. The used classifiers include: Self-Organizing Map (SOM); Classification Trees (CTs); and Support Vector Machines (SVMs) with average classification accuracies of 96.8percent, 95.9percent and 93.7percent obtained for SVMs, SOM, and CTs respectively. FMV was then applied for combining the class memberships from the three classifiers. The main aim is to reduce overlapping regions of different classes for minimizing misclassification errors. The outcomes demonstrate that the overall accuracy as well as commission and omission errors have been improved compared to the best single classifier.