An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion
Data classification is a critical step to convert remotely sensed data into thematic information. Environmental researchers have recently maximized the synergy between passive sensors and LiDAR (Light Detection and Ranging) for land cover classification by means of machine learning. Although object-based paradigm is frequently used to classify high resolution imagery, it often requires a high level of expertise and time effort. Contextual classification may lead to similar results with a decrease in time and costs for non-expert users. This work shows a novel contextual classifier based on a Support Vector Machine (SVM) and an Evolutionary Majority Voting (SVMEMV) to develop thematic maps from LiDAR and imagery data. Subsequently, the performance of SVMEMV is compared to that achieved by a pixel-based SVM as well as to a contextual classified based on SVM and MRF. The classifiers were tested over three different areas of Spain with well differentiated environmental characteristics. Results show that SVM-EMV statistically outperforms the rest (SVM, SVMMRF) for the three datasets obtaining a 77%, 91% and 92% of global accuracy for Trabada, Huelva and Alto Tajo, respectively.
contextual classification lidar data fusion remote sensing machine learning multispectral